Systems and methods for content audience analysis via encoded links

ABSTRACT

The present disclosure is directed to systems and methods for analyzing content audience by generating respective encoded links for content publishers, determining, for each content publisher, statistics related to user interactions with encoded links, and providing user-specific or aggregate information based on the statistics. The server of a content audience analysis system receives multiple interactions with encoded links generated by the content audience analysis system and linked to resources of a first content publisher. The server may identify from the multiple interactions, multiple cookies assigned to unique client devices. The server may identify second content publishers having resources that were accessed by the client devices corresponding to the multiple cookies via encoded links generated by the server. The server may provide to the first content publisher, data corresponding to the second content publishers having resources accessed by client devices that also accessed the resources of the first content publisher.

RELATED APPLICATION

The present application claims the benefit of and priority under 35U.S.C. § 120 as a continuation of U.S. patent application Ser. No.16/523,282, titled “SYSTEMS AND METHODS FOR CONTENT AUDIENCE ANALYSISVIA ENCODED LINKS,” filed Jul. 26, 2019, which claims the benefit of andpriority under 35 U.S.C. § 120 as a continuation of U.S. patentapplication Ser. No. 15/231,457, titled “SYSTEMS AND METHODS FOR CONTENTAUDIENCE ANALYSIS VIA ENCODED LINKS,” filed Aug. 8, 2016, which claimsthe benefit of and priority under 35 U.S.C. § 119(e) to U.S. ProvisionalPatent Application 62/269,361, titled “SYSTEMS AND METHODS FOR CONTENTAUDIENCE ANALYSIS VIA ENCODED LINKS,” filed Dec. 18, 2015, each of whichare incorporated by reference in their entirety.

FIELD OF THE INVENTION

The present application is generally directed to systems and methods foranalyzing online content audience. In particular, the presentapplication is directed to analyzing online content audience via encodedlinks.

BACKGROUND

With the growth of social networking and content on the Internet ingeneral, more and more people are sharing content with other people on aregular basis across multiple online resources. As a result of sharingcontent across the multiple online resources, a multitude of onlinecontent audiences may access the same content from different onlineresources. This sharing may occur with a plurality of different contentpublished by content publishers. This sharing also allows a contentpublisher to understand what the audiences accessing its content areinterested in.

SUMMARY OF THE INVENTION

The present solution provides multiple techniques for identifying,tracking and analyzing user's interactions with content that may beshared across the Internet. Embodiments of the present solutionidentifies and tracks user's interactions with content, such as viaclicking on shortened URLs or links that may be shared among users. Onetechnique of the present solution provides a relevance score of adigital resource based on user's interactions with digital resources.Another technique of the present solution provides relevance searchresults based on user interaction with content. Another technique of thepresent solution identifies phrases that are trending or are temporallypopular based on aggregating multiple users' interactions with anaggregate of content. Yet another technique of the present solutionidentifies trending phrases that are relevant to a unique user. Onetechnique of the present solution provides for tracking influence of auser, based on engagement or clicks driven by that user on contentshared by that user, such as via shortened links. Another technique ofthe present solution provides a recommended set of URLs given an inputURL based on user interactions with a list of related URLs.

In one aspect, the present application is directed to a method forrelevance scoring of a digital resource keyword based on user actionsassociated with a plurality of digital resources. The method scorescontent, URLs, domains, phrases or any entity (all of which are examplesof digital resources) based on an expected relevance to an individualuser which may be based on that user's previous engagement with digitalresources. The method may include receiving, by a server, identificationof a first plurality of actions of a user. Each of the first pluralityof actions may include a click by the user on a link associated with adigital resource of a plurality of digital resources. The server mayreceive identification of a second plurality of actions of the user toshare one or more digital resources of the plurality of digitalresources. The server may identify a plurality of keywords from contentof one or more digital resources of the plurality of digital resources.The server may classify patterns from the first plurality of actions,the second plurality of actions and the plurality of keywords. Theserver may generate, based on the pattern classification, a relevancescore responsive to receiving a user identifier and a digital resourcekeyword.

In some embodiments, the server may receive identification of the firstplurality of actions of the user comprising clicks by the user on anencoded link associated with the digital resource. The encoded link mayprovide a shortened version of the link to the digital resource. Theserver may receive identification of the first plurality of actions ofthe user from one or more cookies associated with the user. The servermay receive identification of the second plurality of actions of theuser comprising forwarding at least a portion of content of the digitalresource to a second user. The server may receive identification of thesecond plurality of actions of the user comprising sharing at least aportion of content from the digital resource in a social networkingsite. The server may identify the plurality of keywords from the contentbased on one or more of the following: text, phrases and meta-data. Theserver may identify the plurality of keywords from analyzing text orphrases in media files, such as video and/or audio files.

In some embodiments, the server may classify patterns from the pluralityof digital resources. The server may match the digital resource keywordto the pattern classification data of the user identified by the useridentifier. The server may receive the user identifier and the digitalresource keyword comprising any of the following: uniform resourcelocator, domain name and a phrase. The server may generate a data scoreidentifying an amount or quality of data classified for the user. Thedigital resource may include any of the following: content, a web page,a uniform resource locator, a domain name and a phrase.

In another aspect, the present application is directed to a system forrelevance scoring of a digital resource keyword based on user actionsassociated with a plurality of digital resources. The system scorescontent, URLs, domains, phrases or any entity (all of which are examplesof digital resources) based on an expected relevance to an individualuser which may be based on that user's previous engagement with digitalresources. The system may include a server receiving identification of afirst plurality of actions associated with digital resources and asecond plurality of actions of user engagement with digital resourcessuch as via viewing of the digital resource by others to whom the usershared the digital resource. Each of the first plurality of actions mayinclude a click by the user on a link associated with a digital resourceof a plurality of digital resources. Each of the second plurality ofactions of the user may include an action to share one or more digitalresources of the plurality of digital resources. A content extractor mayidentify a plurality of keywords from content of one or more digitalresources of the plurality of digital resources. A classifier mayclassify patterns from the first plurality of actions, the secondplurality of actions and the plurality of keywords. In addition, arelevance score generator may generate, responsive to the classifier, arelevance score responsive to receiving a user identifier and a digitalresource keyword.

In some embodiments, the first plurality of actions of the user includesclicks by the user on an encoded link associated with the digitalresource. The encoded link may provide a shortened version of the linkto the digital resource. The server may identify the first plurality ofactions of the user from one or more cookies associated with the user.The second plurality of actions of the user may comprise forwarding atleast a portion of content of the digital resource to a second user. Thesecond plurality of actions of the user may include sharing at least aportion of content from the digital resource in a social networkingsite.

In certain embodiments, the content extractor may identify the pluralityof keywords from the content based on one or more of the following:text, phrases, images and meta-data. The content extractor may identifythe plurality of keywords from media by analyzing text, phrases orcontent of media files, such as video and/or audio files. The relevancescore generator may match, via the classifier, the digital resourcekeyword to the pattern classification data of the user identified by theuser identifier. The classifier may further classify patterns from theplurality of digital resources. The relevance score generator mayreceive the digital resource keyword comprising any of the following:uniform resource locator, domain name and a phrase. The relevance scoregenerator may generate a data score identifying an amount of data of theclassifier for the user. The digital resource may, in some embodiments,comprise any of the following: content, a web page, a uniform resourcelocator, a domain name and a phrase.

In yet another aspect, the present application is directed to a methodfor providing search results based on user interaction with content. Themethod may include receiving, by a server, identification of a pluralityof clicks of encoded uniform resource locator (URL) links. The servermay identify, for each of the plurality of clicks, data about a user whoclicked an encoded URL link and traffic data associated with a devicefrom which the user clicked the encoded URL link. The server may store arecord for each click of the plurality of clicks, the record comprisingdata about the user and traffic data associated with each click. Theserver may determine, based on the records, a relevancy score for eachcontent identified from decoding the encoded URL links. The server maycommunicate, responsive to receiving a request to search content basedon a keyword, a set of search results based on the keyword and therelevancy score. The search results may be based on audience segmentingparameters identified via the request, such as geography, language orother demographic parameters.

In some embodiments, the server decodes each of the encoded URL links.The server may identify data about the user from a cookie communicatedvia a click by the user on the encoded URL link. The server may identifytraffic data comprising one or more of a browser type, a referring website, a source internet protocol address and a destination internetprotocol address. In certain embodiments, the server may determine anengagement score for each content based on a number of clicks receivedvia one or more encoded URL links to the content. Each of the number ofclicks may be weighted based on when received. The server may determinea distribution score for each content based on a number of clicks fromdifferent sources via one or more encoded URL links to the content. Theserver may determine a social score for each content. The server maydetermine a frequency normalization value for each content by extractingkeywords from the content, normalizing the keywords and storing thekeywords and corresponding normalization values into a database.

In certain embodiments, the server may apply a time decay function tothe relevancy score based on the length of time a content has beenstored in a record after being identified from decoding the encoded URLlinks. The server may determine the relevancy score by a combination oftwo or more of a social score, a distribution score, an engagementscore, a frequency normalization value and a time decay function. Insome embodiments, the server may order the set of search results byrelevance score. Although sometimes referred to as scores, these scoremay be considered weights to be applied to determine the relevancescore.

In still another aspect, the present application is directed to a systemfor providing search results based on user interaction with content. Aserver may receive identification of a plurality of clicks of encodeduniform resource locator (URL) links. A click tracker may identify, foreach of the plurality of clicks, data about a user who clicked anencoded URL link and traffic data associated with a device from whichthe user clicked the encoded URL link. A database may store a record foreach click of the plurality of clicks. The record may include data aboutthe user and/or traffic data associated with each click. A relevancyscorer may determine, based on the records, a relevancy score for eachcontent identified from decoding the encoded URL links. The server maycommunicate a set of search results based on the keyword and therelevancy score responsive to receiving a request to search contentbased on a keyword. The server may perform the search based on anyaudience segmentation parameters, such as a geography, language or otherdemographics.

In some embodiments, the server may decode each of the encoded URLlinks. The click tracker may identify data about the user from a cookiecommunicated via a click by the user on the encoded URL link. The clicktracker may identify traffic data comprising one or more of a browsertype, a referring web site, a source internet protocol address and adestination internet protocol address. In certain embodiments, therelevancy scorer determines a distribution score for each content basedon a number of clicks from different sources via one or more encoded URLlinks to the content. The relevancy scorer may determine an engagementscore for each content based on a number of clicks received via one ormore encoded URL links to the content. Each of the number of clicks maybe weighted based on when received.

In some embodiments, the relevancy scorer determines a frequencynormalization value for each content by extracting keywords from thecontent. The relevancy scorer may normalize the keywords and may storethe keywords and corresponding normalization values into a database. Therelevancy scorer may apply a time decay function to the relevancy scorebased on the length of time a content has been stored in a record afterbeing identified from decoding the encoded URL links. The relevancyscorer may determine the relevancy score by a combination of two or moreof a distribution score, an engagement score, a frequency normalizationvalue and a time decay function. The relevancy scorer can generateand/or may order search results by relevance score.

In another aspect, the present application is directed to a method foridentifying trends in phrases based on users interaction with contentcontaining, related to or associated with the phrases. The method mayidentify trending or temporally popular phrases based on aggregatingmultiple users' interactions with an aggregate of content. The methodmay include receiving, by a server, identification of a plurality ofclicks of encoded uniform resource locator (URL) links. The server mayidentify, for each of the plurality of clicks, in content identifiedfrom decoding the encoded URL links, a plurality of phrases thatcorrespond to a predetermined set of keywords. The server may determinea velocity of clicks on content corresponding to each phrase of theplurality of phrases. The server may identify a trend in one or morephrases of the plurality of phrases based on the velocity of clicks. Theserver may receive identification of the plurality of clicks from aplurality of different users via a plurality of different sources.

In some embodiments, the server decodes each of the encoded URL links toobtain a URL to the content. The server may identify the plurality ofphrases in the content based on one of text or meta-data in the content.The server may identify one or more phrases of the plurality of phrasesin the content that deviates from a predetermined norm for the content.The server may determine velocity based on a number of clicks via one ormore encoded URL links within a predetermined time period on contentcorresponding to a phrase. The server may determine velocity based on arate of clicks via one or more encoded URL links to contentcorresponding to a phrase. The server may determine velocity based on achange in rate of clicks via one or more encoded URL links to contentcorresponding to a phrase. In some embodiments, the server enumerates alist of phrases from the plurality of phrases based on increasingvelocity of clicks. The server may enumerate a list of phrases from theplurality of phrases based on decreasing velocity of clicks.

In yet another aspect, the present application is directed to a systemfor identifying trends in phrases based on users interaction withcontent containing, related to or associated with the phrases. Thesystem may identify trending or temporally popular phrases based onaggregating multiple users' interactions with an aggregate of content.The system may include a server receiving identification of a pluralityof clicks of encoded uniform resource locator (URL) links. A contentextractor may identify, for each of the plurality of clicks, in contentidentified from decoding the encoded URL links, a plurality of phrasesthat correspond to a predetermined set of keywords. A trending enginemay determine a velocity of clicks on content corresponding to eachphrase of the plurality of phrases and identifying a trend in one ormore phrases of the plurality of phrases based on the velocity ofclicks.

In some embodiments, the server receives identification of the pluralityof clicks from a plurality of different users via a plurality ofdifferent sources. The server may decode each of the encoded URL linksto obtain a URL to the content. In certain embodiments, the contentextractor identifies the plurality of phrases in the content based onone of text or meta-data in the content. In some embodiments, thecontent extractor identifies a plurality of phrases in the media contentbased on analyzing text or meta-data in a media file, such as videoand/or audio files. The content extractor may identify one or phrases ofthe plurality of phrases in the content that deviates from apredetermined norm for the content. The trending engine may determinevelocity based on a number of clicks via one or more encoded URL linkswithin a predetermined time period on content corresponding to a phrase.The trending engine may determine velocity based on a rate of clicks viaone or more encoded URL links to content corresponding to a phrase. Thetrending engine may determine velocity based on a change in rate ofclicks via one or more encoded URL links to content corresponding to aphrase. The trending engine may enumerate a list of phrases from theplurality of phrases based on increasing velocity of clicks. Thetrending engine may enumerate a list of phrases from the plurality ofphrases based on decreasing velocity of clicks.

In yet another aspect, the present application is directed to a methodfor identifying which phrases are trending across an aggregate of usersthat are relevant to a specific user. The method may include receiving,by a server, identification of a user. The server may identify aplurality of phrases that are trending upwards based on velocity ofclicks to content containing, related to or associated with theplurality of phrases. The server may identify trending or temporallypopular phrases based on aggregating multiple users' interactions withan aggregate of content. The server may determine a relevance score foreach phrase of the plurality of phrases that are trending upwards basedon identification of the user and actions of the user on contentassociated with each phrase, such as user clicking on contentidentifying or related to each phrase. The server may identify one ormore phrases of the plurality of phrases based on relevance score.

In some embodiments, the server may receive identification of the uservia a cookie. The server may identify the plurality of phrases that aretrending upwards above a predetermined threshold. The server mayidentify an enumerated list of the plurality of phrases based ontrending from highest to lowest. The server may determine the relevancescore for each phrases based on a plurality of actions of the user toclick on a link to content user on content related to or identifyingeach phrase. The server may determine the relevance score for eachphrase based on a plurality of actions of the user to share contentassociated with each phrase. The server may select the one or morephrases with the highest relevance score. In some embodiments, theserver selects the one or more phrases with a relevance score greaterthan a predetermined threshold. The server may select the one or morephrases with a relevance score greater than a first predeterminedthreshold and that are trending within a second predetermined threshold.The server may select content to serve the user based on the identifiedone or more phrases.

In yet another aspect, the present application is directed to a systemfor identifying phrases trending across an aggregate of userinteractions that are relevant to a specific user. The system mayinclude a server receiving identification of a user. A trending enginemay identify a plurality of phrases that are trending upwards based onvelocity of clicks from a plurality of user to content containing,associated with or related to the plurality of phrases. The trendingengine may identify trending or temporally popular phrases based onaggregating multiple users' interactions with an aggregate of content. Arelevance scorer may determine for each phrase of the plurality ofphrases that are trending upwards based on identification of the userand actions of the user on content associated with or identifying eachphrase, such as a user clicking on content related to each phrase. Theserver may identify one or more phrases of the plurality of phrasesbased on relevance score.

In some embodiments, the server receives identification of the user viaa cookie. The trending engine may identify the plurality of phrases thatare trending upwards above a predetermined threshold. The trendingengine may identify the plurality of phrases that are trending based onrank ordered relative to other phrases. The trending engine may identifyan enumerated list of the plurality of phrases based on trending fromhighest to lowest. In certain embodiments, the relevance scorerdetermines the relevance score for each phrase based on a plurality ofactions of the user to click on a link to content relating to oridentifying each phrase. The relevance scorer may determine therelevance score for each phrase for each user based on a plurality ofactions of the user to share content associated with each phrase. Theserver may select the identified one or more phrases with the highestrelevance score. The server may select the identified one or morephrases with a relevance score greater than a predetermined threshold.The server may select the identified one or more phrases with arelevance score greater than a first predetermined threshold and thatare trending within a second predetermined threshold. In someembodiments, one of the server or a second server selects content toserve the user based on the identified one or more phrases.

In yet another aspect, the present application is directed to a methodfor tracking influence of a user on content shared via encoded uniformresource locator (URL) links. Measuring influence of a user may identifywhat level of engagement the user drives to content when the user sharescontent with other users, such as via encoded links. A high influencermay be a user who drives a high level of engagement with content whenthe user shares content. A low influencer may be a user who does notdrive a high level of engagement, or otherwise drives a low level ofengagement with content when the user shares content. The method mayinclude receiving, by a server, identification of a user for each of aplurality of encoded uniform resource locator (URL) links. The servermay identify a plurality of keywords from content identified by eachencoded URL link. The server may determine a number of actions via aplurality of users that decoded each encoded URL link of the pluralityof encoded URL links of the user. The server may store, in a profile ofthe user, information on the one or more keywords and the number ofactions.

In some embodiments, the server receives one or more requests from theuser to encode one or more the plurality of encoded URL links. Theserver may identify the user via one of a cookie or a user account withthe server. The server may identify the plurality of keywords from thecontent identified by the decoded URL links based on one or more of thefollowing: text, phrases and meta-data. The server may identify theplurality of keywords from media associated with or in the contentidentified by the decoded URL links based on analyzing text or phrasesin the media file, such as video and/or audio files. The server maydetermine the plurality of actions of the plurality of users on theencoded URLs links from one or more cookies associated with each of theplurality of users. The server may receive identification of theplurality of actions of the plurality of users comprising forwarding byeach of the plurality of the users an encoded URL link. The server mayreceive identification of the plurality of actions of the plurality ofusers comprising sharing by each of the plurality of the users anencoded URL link in one or more social networking sites. In certainembodiments, the server may receive a request for a relevance score forthe user and a keyword. The server may generate the relevance scoreresponsive to the request and one of up weighting or down weighting therelevance score based on the profile of the user. The server maygenerate the relevance score responsive to the request and generatingthe relevance score based on the profile of the user and the profiles ofusers who decoded the encoded URL links of the user.

In still another aspect, the present application is directed to a systemfor tracking influence of a user on content shared via encoded uniformresource locator (URL) links. The system may include a server receivingidentification of a user for each of a plurality of encoded uniformresource locator (URL) links. A content extractor may identify aplurality of keywords from content identified by each encoded URL link.A click tracker determining a number of actions via a plurality of usersthat decoded each encoded URL link of the plurality of encoded URL linksof the user. The server may store in a profile of the user informationon the one or more keywords and the number of actions.

In certain embodiments, the server may receive one or more requests fromthe user to encode one or more of the plurality of encoded URL links.The server may identify the user via one of a cookie or a user accountwith the server. The server may identify the plurality of keywords fromthe content identified by the decoded URL links based on one or more ofthe following: text, phrases and meta-data. The server may identify theplurality of keywords from media associated with or in the contentidentified by the decoded URL links based on analyzing text or phrasesin the media file, such as video and/or audio files. The click trackermay determine the plurality of actions of the plurality of users on theencoded URLs links from one or more cookies associated with each of theplurality of users.

In some embodiments, the plurality of actions of the plurality of usersmay include forwarding by each of the plurality of the users an encodedURL link. The plurality of actions of the plurality of users may includesharing by each of the plurality of the users an encoded URL link in oneor more social networking sites. The server may receive a request for arelevance score for the user and a keyword. A relevance scorer maygenerate the relevance score responsive to the request and one of upweights or down weights the relevance score based on the profile of theuser. A relevance scorer may generate, responsive to the request, therelevance score based on the profile of the user and the profiles ofusers who decoded the encoded URL links of the user.

In still another aspect, the present application is directed to a methodfor providing a recommended list of uniform resource locators (URLs)responsive to a uniform resource locator (URL). The method may includeidentifying, by a server, a plurality of users that clicked on anencoded uniform resource locator (URL) link corresponding to a URL. Theserver may identify a plurality of encoded URL links clicked by each ofthe plurality of users. The server may determine a number of users whoclicked on each encoded URL link of the plurality of encoded URL linksand also clicked on the encoded URL link. The server may enumerate,responsive a request comprising the URL, a list of URLs and theircorresponding score based on the determination, each URL of the list ofURLs corresponding to one of the plurality of encoded URL links.

In certain embodiments, the server may receive identification of a clickof the encoded URL link from each of the plurality of users. The servermay determine a decoded URL corresponding to the encoded URL link. Theserver may identify the user via a cookie. The server may track clickson encoded URL links for each user of the plurality of users. The servermay generate a click co-occurrence map that correlates the plurality ofusers that clicked on the encoded URL link. The server may generate theclick co-occurrence map or co-occurrence map that correlates theplurality of users that clicked on the encoded URL link to the pluralityof encoded URLs link that each of the plurality of users has clicked.The server may communicate a response to the request. The response mayinclude the enumerated list of URLs and their corresponding score. Theserver may enumerate the list of URLS ordered by the number of users whoclicked on the encoded URL link corresponding to the URL and clicked onthe encoded URL link. The server may filter the list of URLs based oncontent or domain.

In another aspect, the present application is directed to a system forproviding a recommended list of uniform resource locators (URLs)responsive to a uniform resource locator (URL). The system may include aserver identifying a plurality of users that clicked on an encodeduniform resource locator (URL) link corresponding to a URL. A clicktracker may identify a plurality of encoded URL links clicked by each ofthe plurality of users. A correlation engine may determine a number ofusers who clicked on each encoded URL link of the plurality of encodedURL links and also clicked on the encoded URL link. The server may,responsive to a request comprising the URL, enumerate a list of URLs.Each URL of the list of URLs may correspond to one of the plurality ofencoded URL links.

In certain embodiments, the server may receive identification of a clickof the encoded URL link from each of the plurality of users. The servermay determine a decoded URL corresponding to the encoded URL link. Incertain embodiments, the server identifies the user via a cookie. Theclick tracker may track clicks on encoded URL links for each user of theplurality of users. The correlation engine may generate a clickcoherency or co-occurrence map that correlates the plurality of usersthat clicked on the encoded URL link. The correlation engine maygenerate the click co-occurrence map that correlates the plurality ofusers that clicked on the encoded URL link to the plurality of encodedURLs link that each of the plurality of users has clicked.

In certain embodiments, the server communicates a response to therequest, the response comprising the enumerated list of URLs and theircorresponding score. The server may enumerate the list of URLS orderedby the number of users who clicked on the encoded URL link correspondingto the URL and clicked on the encoded URL link. The server may filterthe list of URLs based on content identified by the URL.

In another aspect, the present application is directed to a method formonitoring online activities of users. The method may include providing,by a server of a link shortening system, a cookie of the link shorteningsystem to a client device responsive to receiving from a client device,a first request from a first resource to access a first link that isencoded by the link shortening system and linked to a second resource.The server may identify from the first request, the cookie of the linkshortening system, the first resource and the second resource. Theserver may receive from the client device, a second request to access asecond link that is encoded by the link shortening system and linked toa third resource. The server may identify from the second request, thesame cookie provided to the client device and the third resource. Theserver may identify, via the cookie provided to the client device, thatthe client device has accessed the first resource, the second resourceand the third resource.

In some embodiments, the server may identify, from the second request, afourth resource from which the server received the second request. Thefourth resource may be the same as the first resource.

In some embodiments, the server may receive a third request from a fifthresource to access a link that is encoded by the link shortening systemand linked to a sixth resource, and identify, via the cookie provided tothe client device, that the client device has accessed the firstresource, the second resource, the third resource, the fourth resource,the fifth resource and the sixth resource.

In some embodiments, the first request may be received by receiving afirst interaction on the first encoded link, and the second request maybe received by receiving a second interaction on the second encodedlink. The server may provide an output identifying a plurality ofresources accessed by the client device. Responsive to the serverreceiving the second request from the client device, the server mayreceive the cookie included in the second request, update browsing datastored in the cookie to include a source resource and a destinationresource identified by the second request, and transmit, to the clientdevice, the updated cookie.

In some embodiments, the server may provide the cookie responsive todetermining that the first request does not identify a cookie. Theserver may provide a second cookie to a second client device responsiveto receiving a request to access any link encoded by the link shorteningsystem. The server may identify, from the second cookie, a plurality ofresources accessed by the second client device based on links encoded bythe link shortening system.

In still another aspect, the present application is directed to a systemfor monitoring online activities of users. A server of a link shorteningsystem may include a cookie management engine and a resourceidentification engine. The cookie management engine may be configured toprovide a cookie to a client device responsive to receiving from aclient device, a first request from a first resource to access a firstlink that is encoded by the link shortening system and linked to asecond resource. The resource identification engine may be configured toidentify from the first request, the cookie, the first resource and thesecond resource, receive a second request to access a second link thatis encoded by the link shortening system and linked to a third resource,identify from the second request, the same cookie provided to the clientdevice by the cookie management engine and the third resource, andidentify, via the cookie provided to the client device, that the clientdevice has accessed the first resource, the second resource and thethird resource.

In some embodiments, the resource identification engine may be furtherconfigured to identify, from the second request, a fourth resource fromwhich the server received the second request. The fourth resource may bethe same as the first resource.

In some embodiments, the resource identification engine may be furtherconfigured to receive a third request from a fifth resource to access alink that is encoded by the link shortening system and linked to a sixthresource, and identify, via the cookie provided to the client device,that the client device has accessed the first resource, the secondresource, the third resource, the fourth resource, the fifth resourceand the sixth resource.

In some embodiments, the first request may be received by receiving afirst interaction on the first encoded link, and the second request maybe received by receiving a second interaction on the second encodedlink. The resource identification engine may be further configured toprovide an output identifying a plurality of resources accessed by theclient device. Responsive to the server receiving the second requestfrom the client device, the cookie management engine may be configuredto receive the cookie included in the second request, update browsingdata stored in the cookie to include a source resource and a destinationresource identified by the second request, and transmit, to the clientdevice, the updated cookie.

In some embodiments, the cookie management engine may be configured toprovide the cookie responsive to determining that the first request doesnot identify a cookie. The cookie management engine may be configured toprovide a second cookie to a second client device responsive toreceiving a request to access any link encoded by the link shorteningsystem.

In some embodiments, the resource identification module may be furtherconfigured to identify, from the second cookie, a plurality of resourcesaccessed by the second client device based on links encoded by the linkshortening system.

In another aspect, the present application is directed to a system foranalyzing traffic across multiple media channels via encoded links. Aserver of a link shortening system may be configured to receiveidentification of the media channels to distribute a link to a resourceand generate different links encoded by the server and linked to theresource for the identified media channels. The server may be configuredto assign a respective encoded link of the generated encoded links toeach of the identified media channels and determine for each of theidentified media channels, statistics related to the respective encodedlink. The server may also be configured to provide an output comprisingat least a portion of the statistics.

In some embodiments, the statistics may include for each media channel,at least one of a first number of requests to access the resource thatwere received or a second number of different source Uniform ResourceLocators (URLs) from which requests to access the resource werereceived.

In some embodiments, the server may be further configured to assign therespective encoded link to each of the identified media channels byassigning a first encoded link to a first social networking channel, asecond encoded link to a Short Message Service (SMS) channel, and athird encoded link to an email channel.

In some embodiments, the server is configured to provide the output byproviding statistics relating to performance of a Short Message Service(SMS) channel or an email channel. The SMS channel or email channel mayhave a source address that is not a Uniform Resource Locator (URL).

In some embodiments, the server may be configured to identify, for theresource, a first statistic for each of the media channels, andaggregate each of the identified first statistics to generate anaggregate first statistic for the resource. The server may be furtherconfigured to rank each of the media channels based on their respectivefirst statistics. The portion of the statistics relates to a specificmedia channel that is one of social networking channel, Short MessageService (SMS) channel and email channel.

In some embodiments, the server may be further configured to identify acommon source Uniform Resource Locator (URL) across multiple mediachannels. The server may be configured to receive multiple requests toaccess a first resource via a first encoded link linked to the firstresource and generated for a first media channel. The server may beconfigured to identify a common source URL from which requests to accessthe first resource via the first encoded link, among the multiplerequests, were received.

In some embodiments, the media channels may include a first channelcorresponding to a first domain and a second channel corresponding to asecond domain. The media channels may include a first channelcorresponding to a first advertisement of an advertiser and a secondchannel corresponding to a second advertisement of the advertiser.

In still another aspect, the present application is directed to a methodfor traffic across multiple media channels via encoded links. The methodmay include receiving, by a server of a media channel analysis system,identification of the media channels to distribute a link to a resource.The server may generate different encoded links linked to the resourcefor the identified media channels and assign a respective encoded linkof the generated encoded links to each of the identified media channels.The server may also determine, for each of the identified mediachannels, statistics related to the respective encoded link, and providean output comprising at least a portion of the statistics.

In some embodiments, the statistics may include for each media channel,at least one of a first number of requests to access the resource thatwere received or a second number of different source Uniform ResourceLocators (URLs) from which requests to access the resource werereceived. In assigning the respective encoded link to each of theidentified media channels, the server may assign a first encoded link toa first social networking channel, a second encoded link to a ShortMessage Service (SMS) channel, and a third encoded link to an emailchannel.

In some embodiments, in providing the output, the server may providestatistics relating to performance of a Short Message Service (SMS)channel or an email channel. The SMS channel or email channel may have asource address that is not a Uniform Resource Locator (URL). For theresource, a first statistic may be identified for each of the mediachannels. Each of the identified first statistics may be aggregated togenerate an aggregate first statistic for the resource. Each of themedia channels may be ranked based on their respective first statistics.

In some embodiments, the portion of the statistics may relate to aspecific media channel that is one of social networking channel, ShortMessage Service (SMS) channel and email channel. Multiple requests toaccess a first resource may be received via a first encoded link linkedto the first resource and generated for a first media channel. A commonsource Uniform Resource Locator (URL) may be identified from whichrequests to access the first resource via the first encoded link, amongthe plurality of requests, were received.

In some embodiments, the media channels may include a first channelcorresponding to a first domain, a second channel corresponding to asecond domain, a third channel corresponding to a first advertisement ofan advertiser, and a fourth channel corresponding to a secondadvertisement of the advertiser.

In still another aspect, the present application is directed to a systemfor analyzing online content audience. The system may include a serverof a linking system, including a cookie management engine and a contentpublisher identification engine. The cookie management engine may beconfigured to receive a plurality of interactions with encoded linksgenerated by the linking system and linked to resources of a firstcontent publisher, and identify, from the plurality of interactions, aplurality of cookies of the linking system assigned to unique clientdevices. The content publisher identification engine may be configuredto identify second content publishers having resources that wereaccessed by the client devices corresponding to the plurality of cookiesvia encoded links generated by the server, and provide, to the firstcontent publisher, data corresponding to the identified second contentpublishers having resources accessed by client devices that alsoaccessed the resources of the first content publisher.

In some embodiments, the server may further include a link generationengine configured to generate the encoded links linked to the resourcesof the first content publisher. In receiving the plurality ofinteractions, the cookie management engine may be configured to receive,from the client devices, a plurality of requests to access the encodedlinks.

In some embodiments, each request to access an encoded link from aclient device of the client devices may identify i) a source URLidentifying a resource on which the encoded link was presented and ii) acookie of the linking system that is unique to the client device. Thecookie management engine may be further configured to identify, for eachrequest, a source content publisher corresponding to the resourceidentified by the source URL.

In some embodiments, the content publisher identification engine may befurther configured to provide, to the first content publisher, datacorresponding to the identified source content publishers havingresources identified by the source URLs that are accessed by the clientdevices that also accessed the resources of the first content publisher.The cookie management engine may be further configured to store theplurality of requests to access encoded links in a data structure, thedata structure including data identifying the source URL on which theencoded link was presented, the identity of the first content publisher,the destination URL identifying the resource of the first contentpublisher, and the cookie provided to the client device.

In some embodiments, the cookie management engine may be furtherconfigured to determine that the request includes a cookie of thelinking system, and responsive to determining that the request does notinclude a cookie of the linking system, provide, to the client device, aunique cookie of the linking system. The content publisheridentification engine may be further configured to receive, from thefirst content publisher, a request to identify content publishers thathave resources that have been accessed by client devices that alsoaccessed the first content publisher.

In still another aspect, the present application is directed to a methodfor analyzing online content audience. The method may include receiving,by a server of a linking system, a plurality of interactions withencoded links generated by the linking system and linked to resources ofa first content publisher. The server may identify from the plurality ofinteractions, a plurality of cookies of the linking system assigned tounique client devices. The server may identify second content publishershaving resources that were accessed by the client devices correspondingto the plurality of cookies via encoded links generated by the server ofthe linking system. The server may provide, to the first contentpublisher, data corresponding to the identified second contentpublishers having resources accessed by client devices that alsoaccessed the resources of the first content publisher.

In some embodiments, the server may generate the encoded links linked tothe resources of the first content publisher. In receiving the pluralityof interactions, the server may receive, from the client devices, aplurality of requests to access the encoded links. Each request toaccess an encoded link from a client device of the client devices mayidentify i) a source URL identifying a resource on which the encodedlink was presented and ii) a cookie of the linking system that is uniqueto the client device. The server may further identify, for each request,a source content publisher corresponding to the resource identified bythe source URL.

In some embodiments, the server may provide, to the first contentpublisher, data corresponding to the identified source contentpublishers having resources identified by the source URLs that areaccessed by the client devices that also accessed the resources of thefirst content publisher. The server may store, to memory, the pluralityof requests to access encoded links in a data structure, the datastructure including data identifying the source URL on which the encodedlink was presented, the identity of the first content publisher, thedestination URL identifying the resource of the first content publisher,and the cookie provided to the client device.

In some embodiments, the server may determine that the request includesa cookie of the linking system, and responsive to determining that therequest does not include a cookie of the linking system, provide, to theclient device, a unique cookie of the linking system. The server mayfurther receive, from the first content publisher, a request to identifycontent publishers that have resources that have been accessed by clientdevices that also accessed the first content publisher.

In still another aspect, the present application is directed to a methodfor analyzing online content audience. The method may include receiving,by a server of a linking system, a plurality of requests to accessdestination resources of a plurality of content publishers viainteractions with encoded links linked to the destination resources, theencoded links generated by the linking system and presented on sourceresources. The server may identify from the plurality of requests, aplurality of cookies of the linking system assigned to unique clientdevices. The server may identify second resources that were accessed bythe client devices corresponding to the plurality of cookies via encodedlinks generated by the server of the linking system. The server maystore, for each cookie of the plurality of cookies, in memory, dataindicating i) the destination resources of the plurality of contentpublishers accessed by the client device of the cookie, ii) the sourceresources on which encoded links linked to the resources accessed by theclient device of the cookie; and iii) second resources accessed by theclient device of the cookie. The server may provide to a contentpublisher of the plurality of content publishers, statistics derivedfrom the stored data.

In some embodiments, the server may further determine, for a destinationresource of the destination resources, a topic based on the contentincluded in the destination resource, and generate, from the storeddata, a list including one or more of the second resources that alsorelate to the topic based on the content included in the secondresources.

In some embodiments, the server may further provide, to a first contentpublisher of the plurality of content publishers, a list including oneor more domains corresponding to the resources accessed by clientdevices that also accessed resources of the first content publisher. Theserver may further provide, to a first content publisher of theplurality of content publishers, a number of cookies of the linkingsystem that accessed a first resource of the first content publisher anda second resource of a second content publisher of the plurality ofcontent publishers.

In another aspect, the present application is directed to a system forbenchmarking online activity via encoded links generated by an onlineactivity benchmarking system. A server of an online activitybenchmarking system may be configured to identify, for an informationresource, multiple encoded links encoded by the server of the onlineactivity benchmarking system and linked to the information resource. Theserver may be configured to receive, via the identified multiple encodedlinks, multiple requests to access the information resource. The servermay be configured to identify, for each request of the multiple requeststo access the information resource, one or more attributes correspondingto the request. The server may be configured to categorize the multiplerequests to access the information resource based on the one or moreidentified attributes. The server may be further configured to provide,for presentation, an output indicating statistics corresponding to thecategorized multiple requests based on the one or more identifiedattributes.

In some embodiments, the one or more attributes corresponding to eachrequest that is performed by a client device, may include a type of theclient device, a geographic location of the client device, a time of dayat which the request is performed, or a source Uniform Resource Locator(URL) on which the request is performed. The server may be furtherconfigured to identify, from the multiple requests, multiple cookies ofthe online activity benchmarking system assigned to unique clientdevices.

In some embodiments, the server may be configured to identify, from acookie corresponding to each request, one or more attributescorresponding to the request. The server may be further configured toprovide a first number of requests via a first one of the multiple linksacross the at least one attribute of the one or more identifiedattributes, and a second number of requests via a second one of themultiple encoded link across the at least one attribute of the one ormore identified attributes.

In some embodiments, the server may be further configured to rank eachof the first and second encoded links based on their respective numberof requests across the at least one attribute of the one or moreidentified attributes. The server may be configured to generate a firstset of encoded links of the multiple encoded links for presentation onmultiple first source information resources corresponding to a firstvertical, and generate a second set of encoded links of the multipleencoded links for presentation on multiple second source informationresources corresponding to a second vertical different from the firstvertical.

In some embodiments, in categorizing the multiple requests to access theinformation resource based on the one or more identified attributes, theserver may be configured to assign a first set of the multiple requeststo the first vertical and assign a second set of the multiple requeststo the second vertical.

In some embodiments, the server may be further configured to identify,for each request of the first set of the multiple requests assigned tothe first vertical, a cookie of the online activity benchmarking systemassigned to a client device from which the request is received. Theserver may be further configured to update a data structure of thecookie to associate the cookie to the first vertical.

In some embodiments, the server may be further configured to determine afirst number of requests to access the information resource via encodedlinks of the online activity benchmarking system that are displayed onwebpages corresponding to a first vertical. The server may be furtherconfigured to determine a second number of requests to access theinformation resource via encoded links of the online activitybenchmarking system that are displayed on webpages corresponding to asecond vertical. The server may be configured to provide, forpresentation, a visual content item based on at least one of the firstnumber of requests or the second number of requests.

In some embodiments, the server may be further configured to determine afirst number of requests to access the information resource via encodedlinks of the online activity benchmarking system that are received froma first referral source. The server may be configured to determine asecond number of requests to access the information resource via encodedlinks of the online activity benchmarking system that are received froma second referral source. The server may be configured to provide, forpresentation a visual content item based on at least one of the firstnumber of requests or the second number of requests.

In still another aspect, the present application is directed to a methodfor benchmarking online activity via encoded links generated by anonline activity benchmarking system. The method may include identifyingby a server of an online activity benchmarking system, for aninformation resource, multiple encoded links encoded by the server ofthe online activity benchmarking system and linked to the informationresource. Multiple requests to access the information resource may bereceived via the identified multiple encoded links. For each request ofthe multiple requests to access the information resource, one or moreattributes corresponding to the request may be identified. The multiplerequests to access the information resource may be categorized based onthe one or more identified attributes. For presentation, an outputindicating statistics corresponding to the categorized multiple requestsmay be provided based on the one or more identified attributes.

In some embodiments, the one or more attributes corresponding to eachrequest that is performed by a client device, may include a type of theclient device, a geographic location of the client device, a time of dayat which the request is performed, or a source Uniform Resource Locator(URL) on which the request is performed. From the multiple requests,multiple cookies of the online activity benchmarking system assigned tounique client devices may be identified. From a cookie corresponding toeach request, one or more attributes corresponding to the request may beidentified.

In some embodiments, a first number of requests via a first one of themultiple encoded links may be provided across the at least one attributeof the one or more identified attributes, and a second number ofrequests via a second one of the multiple encoded link may be providedacross the at least one attribute of the one or more identifiedattributes. Each of the first and second encoded links may be rankedbased on their respective number of requests across the at least oneattribute of the one or more identified attributes. A first set ofencoded links of the multiple encoded links may be generated forpresentation on multiple first source information resourcescorresponding to a first vertical. A second set of encoded links of themultiple encoded links may be generated for presentation on multiplesecond source information resources corresponding to a second verticaldifferent from the first vertical.

In some embodiments, in categorizing the multiple requests to access theinformation resource based on the one or more identified attributes, afirst set of the multiple requests may be assigned to the first verticaland a second set of the multiple requests may be assigned to the secondvertical. For each request of the first set of the multiple requestsassigned to the first vertical, a cookie of the online activitybenchmarking system assigned to a client device from which the requestis received may be identified. A data structure of the cookie may beupdated to associate the cookie to the first vertical.

In some embodiments, a first number of requests to access theinformation resource via encoded links of the online activitybenchmarking system that are displayed on webpages corresponding to afirst vertical may be determined. A second number of requests to accessthe information resource via encoded links of the online activitybenchmarking system that are displayed on webpages corresponding to asecond vertical may be determined. In providing, for presentation, anoutput indicating statistics corresponding to the categorized multiplerequests based on the one or more identified attributes, forpresentation, a visual content item may be provided based on at leastone of the first number of requests or the second number of requests.

In some embodiments, a first number of requests to access theinformation resource via encoded links of the online activitybenchmarking system that are received from a first referral source maybe determined. A second number of requests to access the informationresource via encoded links of the online activity benchmarking systemthat are received from a second referral source may be determined. Inproviding, for presentation, an output indicating statisticscorresponding to the categorized multiple requests based on the one ormore identified attributes, for presentation, a visual content item maybe provided based on at least one of the first number of requests or thesecond number of requests.

The details of various embodiments of the invention are set forth in theaccompanying drawings and the description below.

BRIEF DESCRIPTION OF DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe present invention will become more apparent and better understood byreferring to the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1A is a block diagram of an embodiment of a network environment fora client to access servers;

FIGS. 1B and 1C are block diagrams of embodiments of a computing device;

FIG. 2 is a diagram of an embodiment of a system to shorten, share andtrack links;

FIG. 3A is a block diagram of an embodiment of a system for relevancescoring of a digital resource;

FIG. 3B is a flow diagram of an embodiment of a method for relevancescoring of a digital resource;

FIG. 4A is a block diagram of an embodiment of a system for providingsearch results based on user interaction with content;

FIG. 4B is a flow diagram of an embodiment of a method for providingsearch results based on user interaction with content;

FIG. 5A is a block diagram of an embodiment of a system for identifyingtrends in phrases of content;

FIG. 5B is a flow diagram of an embodiment of a method for identifyingtrends in phrases of content;

FIG. 6A is a block diagram of an embodiment of a system for identifyingtrending and relevance of phrases;

FIG. 6B is a flow diagram of an embodiment of a method for identifyingtrending and relevance of phrases;

FIG. 7A is a block diagram of an embodiment of a system for trackinginfluence of a user on content shared via encoded uniform resourcelocator (URL) links;

FIG. 7B is a flow diagram of an embodiment of a method for trackinginfluence of a user on content shared via encoded uniform resourcelocator (URL) links;

FIG. 8A is a block diagram of an embodiment of a system for providing arecommended list of uniform resource locators (URLs) responsive to auniform resource locator (URL);

FIG. 8B is a flow diagram of an embodiment of a method for providing arecommended list of uniform resource locators (URLs) responsive to auniform resource locator (URL);

FIG. 9A is a diagram of an embodiment of a system to monitor onlineactivities of users via cookies of a linking system;

FIG. 9B is a flow diagram of an embodiment of a method for monitoringonline activities of users via cookies of a linking system;

FIG. 10A is a diagram of an embodiment of a system to analyze multiplemedia channels via encoded links;

FIG. 10B is a flow diagram of an embodiment of a method for analyzingtraffic across multiple media channels via encoded links;

FIG. 11A is a diagram of an embodiment of a system to analyze onlinecontent audience via encoded links;

FIG. 11B is a diagram of an embodiment of a database includinginformation or data associated with an interaction on an encoded link;

FIG. 11C is a flow diagram of an embodiment of a method for analyzingonline content audience via encoded links;

FIG. 12A is a diagram of an embodiment of a system to benchmark onlineactivity via encoded links;

FIG. 12B is an example statistics output of an embodiment of a system tobenchmark online activity via encoded links; and

FIG. 12C is a flow diagram of an embodiment of a method for benchmarkingonline activity via encoded links.

In the drawings, like reference numbers generally indicate identical,functionally similar, and/or structurally similar elements.

DETAILED DESCRIPTION

For purposes of reading the description of the various embodimentsbelow, the following enumeration of the sections of the specificationand their respective contents may be helpful:

-   -   Section A describes a network and computing environment which        may be useful for practicing embodiments described herein;    -   Section B describes embodiments of a system to shorten, track        and analyze links;    -   Section C describes embodiments of systems and methods for        relevance scoring of a digital resource;    -   Section D describes embodiments of systems and methods for        providing search results based on user interaction with content;    -   Section E describes embodiments of systems and methods for        identifying trends in phrases of content;    -   Section F describes embodiments of systems and methods for        identifying trending and relevance of phrases for a user;    -   Section G describes embodiments of systems and methods for        tracking influence of a user on content shared via encoded        uniform resource locator (URL) link;    -   Section H describes embodiments of systems and methods for        providing recommended list of uniform resource locators (URLs)        responsive to a uniform resource locator (URL);    -   Section I describes embodiments of systems and methods for        monitoring online activities of users via cookies of a linking        system;    -   Section J describes embodiments of systems and methods for        analyzing traffic across multiple media channels via encoded        links;    -   Section K describes embodiments of systems and methods for        analyzing online content audience via encoded links; and    -   Section L describes embodiments of systems and methods for        benchmarking online activity via encoded links.

A. Network and Computing Environment

Prior to discussing the specifics of embodiments of the systems andmethods of server and/or client, it is helpful to discuss the networkand computing environments in which such embodiments may be deployed.Referring to FIG. 1A, an embodiment of a network environment isdepicted. In brief overview, the network environment includes one ormore clients 102 a-102 n (also generally referred to as local machine(s)102, client(s) 102, client node(s) 102, client machine(s) 102, clientcomputer(s) 102, client device(s) 102, endpoint(s) 102, or endpointnode(s) 102) in communication with one or more servers 106 a-106 n (alsogenerally referred to as server(s) 106, node 106, or remote machine(s)106) via one or more networks 104. In some embodiments, a client 102 hasthe capacity to function as both a client node seeking access toresources provided by a server and as a server providing access tohosted resources for other clients 102 a-102 n.

Although FIG. 1A shows a network 104 between the clients 102 and theservers 106, the clients 102 and the servers 106 may be on the samenetwork 104. The network 104 can be a local-area network (LAN), such asa company Intranet, a metropolitan area network (MAN), or a wide areanetwork (WAN), such as the Internet or the World Wide Web. In someembodiments, there are multiple networks 104 between the clients 102 andthe servers 106. In one of these embodiments, a network 104′ (not shown)may be a private network and a network 104 may be a public network. Inanother of these embodiments, a network 104 may be a private network anda network 104′ a public network. In still another of these embodiments,networks 104 and 104′ may both be private networks.

The network 104 may be any type and/or form of network and may includeany of the following: a point-to-point network, a broadcast network, awide area network, a local area network, a telecommunications network, adata communication network, a computer network, an ATM (AsynchronousTransfer Mode) network, a SONET (Synchronous Optical Network) network, aSDH (Synchronous Digital Hierarchy) network, a wireless network and awireline network. In some embodiments, the network 104 may comprise awireless link, such as an infrared channel or satellite band. Thetopology of the network 104 may be a bus, star, or ring networktopology. The network 104 may be of any such network topology as knownto those ordinarily skilled in the art capable of supporting theoperations described herein. The network may comprise mobile telephonenetworks utilizing any protocol or protocols used to communicate amongmobile devices, including AMPS, TDMA, CDMA, GSM, GPRS or UMTS. In someembodiments, different types of data may be transmitted via differentprotocols. In other embodiments, the same types of data may betransmitted via different protocols.

In some embodiments, the system may include multiple, logically-groupedservers 106. In one of these embodiments, the logical group of serversmay be referred to as a server farm 38 or a machine farm 38. In anotherof these embodiments, the servers 106 may be geographically dispersed.In other embodiments, a machine farm 38 may be administered as a singleentity. In still other embodiments, the machine farm 38 includes aplurality of machine farms 38. The servers 106 within each machine farm38 can be heterogeneous—one or more of the servers 106 or machines 106can operate according to one type of operating system platform (e.g.,WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Wash.), whileone or more of the other servers 106 can operate on according to anothertype of operating system platform (e.g., Unix or Linux).

In one embodiment, servers 106 in the machine farm 38 may be stored inhigh-density rack systems, along with associated storage systems, andlocated in an enterprise data center. In this embodiment, consolidatingthe servers 106 in this way may improve system manageability, datasecurity, the physical security of the system, and system performance bylocating servers 106 and high performance storage systems on localizedhigh performance networks. Centralizing the servers 106 and storagesystems and coupling them with advanced system management tools allowsmore efficient use of server resources.

The servers 106 of each machine farm 38 do not need to be physicallyproximate to another server 106 in the same machine farm 38. Thus, thegroup of servers 106 logically grouped as a machine farm 38 may beinterconnected using a wide-area network (WAN) connection or ametropolitan-area network (MAN) connection. For example, a machine farm38 may include servers 106 physically located in different continents ordifferent regions of a continent, country, state, city, campus, or room.Data transmission speeds between servers 106 in the machine farm 38 canbe increased if the servers 106 are connected using a local-area network(LAN) connection or some form of direct connection. Additionally, aheterogeneous machine farm 38 may include one or more servers 106operating according to a type of operating system, while one or moreother servers 106 execute one or more types of hypervisors rather thanoperating systems. In these embodiments, hypervisors may be used toemulate virtual hardware, partition physical hardware, virtualizephysical hardware, and execute virtual machines that provide access tocomputing environments. Hypervisors may include those manufactured byVMWare, Inc., of Palo Alto, Calif.; the Xen hypervisor, an open sourceproduct whose development is overseen by Citrix Systems, Inc.; theVirtualServer or virtual PC hypervisors provided by Microsoft or others.

Management of the machine farm 38 may be de-centralized. For example,one or more servers 106 may comprise components, subsystems and modulesto support one or more management services for the machine farm 38. Inone of these embodiments, one or more servers 106 provide functionalityfor management of dynamic data, including techniques for handlingfailover, data replication, and increasing the robustness of the machinefarm 38. Each server 106 may communicate with a persistent store and, insome embodiments, with a dynamic store.

Server 106 may be a file server, application server, web server, proxyserver, appliance, network appliance, gateway, gateway, gateway server,virtualization server, deployment server, SSL VPN server, or firewall.In one embodiment, the server 106 may be referred to as a remote machineor a node. In another embodiment, a plurality of nodes 290 may be in thepath between any two communicating servers.

The client 102 and server 106 may be deployed as and/or executed as anytype and form of computing device, such as a computer, network device orappliance capable of communicating on any type and form of network andperforming the operations described herein. FIGS. 1B and 1C depict blockdiagrams of a computing device 100 useful for practicing an embodimentof the client 102 or a server 106. As shown in FIGS. 1B and 1C, eachcomputing device 100 includes a central processing unit 121, and a mainmemory unit 122. As shown in FIG. 1B, a computing device 100 may includea storage device 128, an installation device 116, a network interface118, an I/O controller 123, display devices 124 a-102 n, a keyboard 126and a pointing device 127, such as a mouse. The storage device 128 mayinclude, without limitation, an operating system, software, and softwarefor sharing, tracking and analyzing links 120. As shown in FIG. 1C, eachcomputing device 100 may also include additional optional elements, suchas a memory port 103, a bridge 170, one or more input/output devices 130a-130 n (generally referred to using reference numeral 130), and a cachememory 140 in communication with the central processing unit 121.

The central processing unit 121 is any logic circuitry that responds toand processes instructions fetched from the main memory unit 122. Inmany embodiments, the central processing unit 121 is provided by amicroprocessor unit, such as: those manufactured by Intel Corporation ofMountain View, Calif.; those manufactured by Motorola Corporation ofSchaumburg, Ill.; those manufactured by Transmeta Corporation of SantaClara, Calif.; the RS/6000 processor, those manufactured byInternational Business Machines of White Plains, N.Y.; or thosemanufactured by Advanced Micro Devices of Sunnyvale, Calif. Thecomputing device 100 may be based on any of these processors, or anyother processor capable of operating as described herein.

Main memory unit 122 may be one or more memory chips capable of storingdata and allowing any storage location to be directly accessed by themicroprocessor 121, such as Static random access memory (SRAM), BurstSRAM or SynchBurst SRAM (BSRAM), Dynamic random access memory (DRAM),Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended DataOutput RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), BurstExtended Data Output DRAM (BEDO DRAM), Enhanced DRAM (EDRAM),synchronous DRAM (SDRAM), JEDEC SRAM, PC100 SDRAM, Double Data RateSDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), SyncLink DRAM (SLDRAM),Direct Rambus DRAM (DRDRAM), or Ferroelectric RAM (FRAM). The mainmemory 122 may be based on any of the above described memory chips, orany other available memory chips capable of operating as describedherein. In the embodiment shown in FIG. 1B, the processor 121communicates with main memory 122 via a system bus 150 (described inmore detail below). FIG. 1C depicts an embodiment of a computing device100 in which the processor communicates directly with main memory 122via a memory port 103. For example, in FIG. 1C the main memory 122 maybe DRDRAM.

FIG. 1C depicts an embodiment in which the main processor 121communicates directly with cache memory 140 via a secondary bus,sometimes referred to as a backside bus. In other embodiments, the mainprocessor 121 communicates with cache memory 140 using the system bus150. Cache memory 140 typically has a faster response time than mainmemory 122 and is typically provided by SRAM, BSRAM, or EDRAM. In theembodiment shown in FIG. 1C, the processor 121 communicates with variousI/O devices 130 via a local system bus 150. Various buses may be used toconnect the central processing unit 121 to any of the I/O devices 130,including a VESA VL bus, an ISA bus, an EISA bus, a MicroChannelArchitecture (MCA) bus, a PCI bus, a PCI-X bus, a PCI-Express bus, or aNuBus. For embodiments in which the I/O device is a video display 124,the processor 121 may use an Advanced Graphics Port (AGP) to communicatewith the display 124. FIG. 1C depicts an embodiment of a computer 100 inwhich the main processor 121 communicates directly with I/O device 130 bvia HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology.FIG. 1C also depicts an embodiment in which local busses and directcommunication are mixed: the processor 121 communicates with I/O device130 a using a local interconnect bus while communicating with I/O device130 b directly.

A wide variety of I/O devices 130 a-130 n may be present in thecomputing device 100. Input devices include keyboards, mice, trackpads,trackballs, microphones, dials, and drawing tablets. Output devicesinclude video displays, speakers, inkjet printers, laser printers, anddye-sublimation printers. The I/O devices may be controlled by an I/Ocontroller 123 as shown in FIG. 1B. The I/O controller may control oneor more I/O devices such as a keyboard 126 and a pointing device 127,e.g., a mouse or optical pen. Furthermore, an I/O device may alsoprovide storage and/or an installation medium 116 for the computingdevice 100. In still other embodiments, the computing device 100 mayprovide USB connections (not shown) to receive handheld USB storagedevices such as the USB Flash Drive line of devices manufactured byTwintech Industry, Inc. of Los Alamitos, Calif.

Referring again to FIG. 1B, the computing device 100 may support anysuitable installation device 116, such as a floppy disk drive forreceiving floppy disks such as 3.5-inch, 5.25-inch disks or ZIP disks, aCD-ROM drive, a CD-R/RW drive, a DVD-ROM drive, a flash memory drive,tape drives of various formats, USB device, hard-drive or any otherdevice suitable for installing software and programs. The computingdevice 100 may further comprise a storage device, such as one or morehard disk drives or redundant arrays of independent disks, for storingan operating system and other related software, and for storingapplication software programs such as any program related to thesoftware 120 for sharing, tracking and analyzing links. Optionally, anyof the installation devices 116 could also be used as the storagedevice. Additionally, the operating system and the software can be runfrom a bootable medium, for example, a bootable CD, such as KNOPPIX, abootable CD for GNU/Linux that is available as a GNU/Linux distributionfrom knoppix.net.

Furthermore, the computing device 100 may include a network interface118 to interface to the network 104 through a variety of connectionsincluding, but not limited to, standard telephone lines, LAN or WANlinks (e.g., 802.11, T1, T3, 56 kb, X.25, SNA, DECNET), broadbandconnections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet,Ethernet-over-SONET), wireless connections, or some combination of anyor all of the above. Connections can be established using a variety ofcommunication protocols (e.g., TCP/IP, IPX, SPX, NetBIOS, Ethernet,ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), RS232, IEEE802.11, IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, CDMA, GSM, WiMax anddirect asynchronous connections). In one embodiment, the computingdevice 100 communicates with other computing devices 100′ via any typeand/or form of gateway or tunneling protocol such as Secure Socket Layer(SSL) or Transport Layer Security (TLS). The network interface 118 maycomprise a built-in network adapter, network interface card, PCMCIAnetwork card, card bus network adapter, wireless network adapter, USBnetwork adapter, modem or any other device suitable for interfacing thecomputing device 100 to any type of network capable of communication andperforming the operations described herein.

In some embodiments, the computing device 100 may comprise or beconnected to multiple display devices 124 a-124 n, which each may be ofthe same or different type and/or form. As such, any of the I/O devices130 a-130 n and/or the I/O controller 123 may comprise any type and/orform of suitable hardware, software, or combination of hardware andsoftware to support, enable or provide for the connection and use ofmultiple display devices 124 a-124 n by the computing device 100. Forexample, the computing device 100 may include any type and/or form ofvideo adapter, video card, driver, and/or library to interface,communicate, connect or otherwise use the display devices 124 a-124 n.In one embodiment, a video adapter may comprise multiple connectors tointerface to multiple display devices 124 a-124 n. In other embodiments,the computing device 100 may include multiple video adapters, with eachvideo adapter connected to one or more of the display devices 124 a-124n. In some embodiments, any portion of the operating system of thecomputing device 100 may be configured for using multiple displays 124a-124 n. In other embodiments, one or more of the display devices 124a-124 n may be provided by one or more other computing devices, such ascomputing devices 100 a and 100 b connected to the computing device 100,for example, via a network. These embodiments may include any type ofsoftware designed and constructed to use another computer's displaydevice as a second display device 124 a for the computing device 100.One ordinarily skilled in the art will recognize and appreciate thevarious ways and embodiments that a computing device 100 may beconfigured to have multiple display devices 124 a-124 n.

In further embodiments, an I/O device 130 may be a bridge between thesystem bus 150 and an external communication bus, such as a USB bus, anApple Desktop Bus, an RS-232 serial connection, a SCSI bus, a FireWirebus, a FireWire 800 bus, an Ethernet bus, an AppleTalk bus, a GigabitEthernet bus, an Asynchronous Transfer Mode bus, a HIPPI bus, a SuperHIPPI bus, a SerialPlus bus, a SCI/LAMP bus, a FibreChannel bus, aSerial Attached small computer system interface bus, or a HDMI bus.

A computing device 100 of the sort depicted in FIGS. 1B and 1C typicallyoperates under the control of operating systems, which controlscheduling of tasks and access to system resources. The computing device100 can be running any operating system such as any of the versions ofthe MICROSOFT WINDOWS operating systems, the different releases of theUnix and Linux operating systems, any version of the MAC OS forMacintosh computers, any embedded operating system, any real-timeoperating system, any open source operating system, any proprietaryoperating system, any operating systems for mobile computing devices, orany other operating system capable of running on the computing deviceand performing the operations described herein. Typical operatingsystems include, but are not limited to: WINDOWS 3.x, WINDOWS 95,WINDOWS 98, WINDOWS 2000, WINDOWS NT 3.51, WINDOWS NT 4.0, WINDOWS CE,WINDOWS MOBILE, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, all of whichare manufactured by Microsoft Corporation of Redmond, Wash.; MAC OS,manufactured by Apple Computer of Cupertino, Calif.; OS/2, manufacturedby International Business Machines of Armonk, N.Y.; and Linux, afreely-available operating system distributed by Caldera Corp. of SaltLake City, Utah, or any type and/or form of a Unix operating system,among others.

The computer system 100 can be any workstation, telephone, desktopcomputer, laptop or notebook computer, server, handheld computer, mobiletelephone or other portable telecommunications device, media playingdevice, a gaming system, mobile computing device, or any other typeand/or form of computing, telecommunications or media device that iscapable of communication. The computer system 100 has sufficientprocessor power and memory capacity to perform the operations describedherein. For example, the computer system 100 may comprise a device ofthe IPOD, IPHONE, or APPLE TV family of devices manufactured by AppleComputer of Cupertino, Calif., a PLAYSTATION 2, PLAYSTATION 3, orPERSONAL PLAYSTATION PORTABLE (PSP) device manufactured by the SonyCorporation of Tokyo, Japan, a NINTENDO DS, NINTENDO GAMEBOY, NINTENDOGAMEBOY ADVANCED, NINTENDO REVOLUTION, or a NINTENDO WII devicemanufactured by Nintendo Co., Ltd., of Kyoto, Japan, an XBOX or XBOX 360device manufactured by the Microsoft Corporation of Redmond, Wash.

In some embodiments, the computing device 100 may have differentprocessors, operating systems, and input devices consistent with thedevice. For example, in one embodiment, the computing device 100 is aTREO 180, 270, 600, 650, 680, 700p, 700w, or 750 smart phonemanufactured by Palm, Inc. In some of these embodiments, the TREO smartphone is operated under the control of the PalmOS operating system andincludes a stylus input device as well as a five-way navigator device.

In other embodiments the computing device 100 is a mobile device, suchas a JAVA-enabled cellular telephone or personal digital assistant(PDA), such as the i55sr, i58sr, i85s, i88s, i90c, i95c1, or the im1100,all of which are manufactured by Motorola Corp. of Schaumburg, Ill., the6035 or the 7135, manufactured by Kyocera of Kyoto, Japan, or the i300or i330, manufactured by Samsung Electronics Co., Ltd., of Seoul, Korea.In some embodiments, the computing device 100 is a mobile devicemanufactured by Nokia of Finland, or by Sony Ericsson MobileCommunications AB of Lund, Sweden.

In still other embodiments, the computing device 100 is a Blackberryhandheld or smart phone, such as the devices manufactured by Research InMotion Limited, including the Blackberry 7100 series, 8700 series, 7700series, 7200 series, the Blackberry 7520, or the Blackberry Pearl 8100.In yet other embodiments, the computing device 100 is a smart phone,Pocket PC, Pocket PC Phone, or other handheld mobile device supportingMicrosoft Windows Mobile Software. Moreover, the computing device 100can be any workstation, desktop computer, laptop or notebook computer,server, handheld computer, mobile telephone, any other computer, orother form of computing or telecommunications device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein.

In some embodiments, the computing device 100 is a digital audio player.In one of these embodiments, the computing device 100 is a digital audioplayer such as the Apple IPOD, IPOD Touch, and IPOD NANO lines ofdevices, manufactured by Apple Computer of Cupertino, Calif. In anotherof these embodiments, the digital audio player may function as both aportable media player and as a mass storage device. In otherembodiments, the computing device 100 is a digital audio player such asthe DigitalAudimpression opportunity layer Select MP3 players,manufactured by Samsung Electronics America, of Ridgefield Park, N.J.,or the Motorola m500 or m25 Digital Audio Players, manufactured byMotorola Inc. of Schaumburg, Ill. In still other embodiments, thecomputing device 100 is a portable media player, such as the Zen VisionW, the Zen Vision series, the Zen Portable Media Center devices, or theDigital MP3 line of MP3 players, manufactured by Creative TechnologiesLtd. In yet other embodiments, the computing device 100 is a portablemedia player or digital audio player supporting file formats including,but not limited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AIFF, Audibleaudiobook, Apple Lossless audio file formats and .mov, .m4v, and .mp4MPEG-4 (H.264/MPEG-4 AVC) video file formats.

In some embodiments, the communications device 102 includes acombination of devices, such as a mobile phone combined with a digitalaudio player or portable media player. In one of these embodiments, thecommunications device 102 is a smartphone, for example, an iPhonemanufactured by Apple Computer, or a Blackberry device, manufactured byResearch In Motion Limited. In yet another embodiment, thecommunications device 102 is a laptop or desktop computer equipped witha web browser and a microphone and speaker system, such as a telephonyheadset. In these embodiments, the communications devices 102 areweb-enabled and can receive and initiate phone calls. In otherembodiments, the communications device 102 is a Motorola RAZR orMotorola ROKR line of combination digital audio players and mobilephones.

In some embodiments, the status of one or more machines 102, 106 in thenetwork 104 is monitored, generally as part of network management. Inone of these embodiments, the status of a machine may include anidentification of load information (e.g., the number of processes on themachine, CPU and memory utilization), of port information (e.g., thenumber of available communication ports and the port addresses), or ofsession status (e.g., the duration and type of processes, and whether aprocess is active or idle). In another of these embodiments, thisinformation may be identified by a plurality of metrics, and theplurality of metrics can be applied at least in part towards decisionsin load distribution, network traffic management, and network failurerecovery as well as any aspects of operations of the present solutiondescribed herein. Aspects of the operating environments and componentsdescribed above will become apparent in the context of the systems andmethods disclosed herein.

B. System for Shortening, Sharing and Tracking Links

Referring now to FIG. 2, embodiments of a system 120 for shortening,sharing and tracking links is depicted. In brief overview, a linkingsystem 120 executes on one or more server(s) 106A-N and may be accessedby a plurality of clients 102 a-102N via a network 104. The linkingsystem 120 may include a link encoder 210 that shortens a link, such asa uniform resource locator (URL) 205 to a resource on a destinationserver 106. The link encoder may encode (e.g., shorten) the linkresponsive to a request to shorten the URL 205. The client may include alink system API or application 225A-225N to interface with the linkingsystem 120 and request to shorten the link. The request may include acookie 255 identifying user and client information. The link encoder 210may generate or otherwise provide an encoded URL 211 to a client. Thelink encoder may store in a database 230 information about the encodingof the URL and the URL 205. The user tracker 215 may track informationabout the user, such as via the cookie 255 and store the information inthe database 230.

Via the browser of the client, a user may click on or otherwise activate250 the encoded URL 211 which directs the browser to the linking system120. The click action 250 may be a request to decode the URL. The clickaction or request thereof may include a cookie 255′ which provides userand client information. The link decoder 212 may decode the encoded URL211, such as via database 230. For example, the link decoder may performa lookup of the URL corresponding to the shortened or encoded URL. Thelinking system, such as via decoder 212, may send a redirect, such as anHypertext Transfer Protocol (HTTP) redirect (e.g., 301 redirect), to theclient to the decoded URL 205. The browser of the client may access orbe directed to the URL 205 of the link destination server 106. The clicktracker 220 may track user actions on the encoded URL, such as when theencoded URL was clicked, from what source and by what user and storesuch tracking in the database 230. The click tracker and/or user trackermay track user information from the cookies 255′ in connection with orassociated with the click action 250. The click tracker and/or usertracker may track and store user the referrer information from therequest.

A user of client 102 a may share via email, web-site posting, socialnetworking, etc. the encoded URL 211 to any one or more other users,such as users of clients 102 b-102N. Any of these users may click on oractivate the encoded URL 211. The plurality of click actions on theencoded URL provide a stream of requests from user click actions todecode the encoded URL, which may be generally referred to as a clickstream 250′. The linking system via link decoder 212 may decode theencoded URL and redirect each of the clients to the URL 205. The usertracker and click tracker may track information on the user and theclick actions of the client stream 250′ in the database. The clickanalyzer 235 may provide metrics on the click actions of the encodedURL, such as the number of clicks, the times of clicks and the sourcesof the clicks.

In further details, the linking system 120 may comprise an application,program, library, process, service, script, task or any type and form ofexecutable instructions executable or executing on a device. The linkingsystem may operate on a plurality of servers 106A-106N. The linkingsystem may comprise logic, function, and operations for shortening,sharing and tracking links, such as URLs. The linking system maycomprise application programming interfaces, such as web services, XML,Jason (JSON), etc. for accessing the functionality, operations and/ordata of the linking system. The linking system may include one or moremodules, components or executables for providing these APIs andperforming the function and operations described herein. For example, insome embodiments, the linking system may include a link encoder 210, alink decoder 212, a user tracker 215, a click tracker 220 and a clickanalyzer 235. The modules, components or executables of the linkingsystem may operate in a client/server architecture. The modules,components or executables of the linking system may operate in adistributed manner across multiple devices.

The linking system may include, operate, communicate or interface with alinking system API or application 225A-225N (generally referred to as225). In some embodiments, an application 225 may execute on the clientthat communicates with or interfaces to the linking system to encode anddecode URLs. In some embodiments, an application 225 may include anyportion of the linking system. In some embodiments, the application maybe a mobile application, generally referred to as an app, executing on amobile device, such as a smart phone or tablet device. In someembodiments, the application may include an add-on, extension, script,ActiveX control, applet, widget or other types and forms of executableinstructions executed by or in a browser. In some embodiments, theapplication may include, use or call one or more APIs to the linkingsystem. The application may be programmed to programmatically integratethe linking system, or interface thereto, into the application. Via theone or more APIs, the application may access data from the linkingsystem. Via the one or more APIs, the application may perform or executeany of the functions or operations of the linking system. Via the one ormore APIs, the application may perform or execute any of the systems andmethods described herein.

The link encoder may include an application, program, library, process,service, script, task or any type and form of executable instructionsfor encoding a link. The link encoder may shorten a URL. The encoded URLmay be referred to or be a shortened URL. Creating a shortened link maybe referred to as encoding. The link encoder may shorten the URL to apredetermined string length or to a predetermined number of characters.The link encoder may shorten the URL to a length determined responsiveto the length of the URL to be encoded. The link encoder may encode theURL into an encoded URL using an encoding scheme. In some embodiments,the link encoder applies a hash to the URL to generate or produce theencoded URL. In some embodiments, the encoded URL is a hash or hashcode. In some embodiments, the link encoder transforms the URL using atransformation function, such as a reversible transformation function.In some embodiments, the link encoder removes a portion of the URL. Insome embodiments, the link encoder rewrites a portion of the URL with aportion of another URL. In some embodiments, the link encoder encryptsthe URL or a portion of the URL using one or more encryption keys. Insome embodiments, the link encoder generates a unique identifier for theencoded URL in which the unique identifier uniquely identifies the URL.In some embodiments, the link encoder obfuscates information from theoriginal URL, such as information relating to a directory structure ofthe server from the URL. The link encoder may encode the URL into anencoded URL that comprises a domain name hosted by or recognized by thelinking system or any server thereof. The link encoder may encode theURL into an encoded URL that comprises a domain name configured,specified or identified by a user, such as a domain name of an entitythat is a user of the linking system. The link encoder may encode theURL to identify a URL of the linking system, such as a landing page orintermediate page of the linking system. In some embodiments, the linkencoder may encode the URL to be resolved to an intermediate URL or pageof the linking system prior to being redirected by the linking system tothe URL after decoding.

The link decoder may include an application, program, library, process,service, script, task or any type and form of executable instructionsfor decoding an encoded link. The link decoder may be designed andconstructed to decode, un-shorten, generate, produce or otherwiseprovide the original URL corresponding to the encoded URL. Clicking on ashortened link may be referred to or called decoding. In someembodiments, the link decoder determines the URL from the encoded URLvia lookup in the database. In some embodiments, the link decoder usesthe encoded URL as an index to look up the URL in the database. In someembodiments, the link decoder uses the encoded URL as a hash index intoa has table of the database. In some embodiments, the link decoder usersthe encoded URL or a portion thereof as a unique identifier to the URLstored in memory, storage of database of the linking system. In someembodiments, the link decoder uses a decoding scheme designed andconstructed to perform the reverse of the encoding scheme or otherwiseproduce or generate the original input (e.g., the URL) to the encodingscheme. In some embodiments, the link decoder applies a reversetransformation function to the encoded URL. In some embodiments, thelink decoder replaces a portion of the encoded URL with a portion of theURL. In some embodiments, the link decoder un-obfuscates information inthe encoded URL to a portion of the original URL. In some embodiments,the link decoder replaces a domain name of the encoded URL with a domainname of the URL.

In some embodiments, the linking system, such as via link decoder,generates, issues or communicates a redirect responsive to receipt of anencoded URL and/or decoding the encoded URL. In some embodiments, thelinking system issues any type of 3XX HTTP redirect. In HTTP, a redirectis a response with a status code beginning with a 3XX that induces abrowser to go to another location. In some embodiments, the response orstatus code includes an annotation describing the reason, which allowsfor the correct subsequent action (such as changing links in the case ofcode 301, a permanent change of address). In some embodiments, thelinking system issues a 301 type of HTTP redirect. In some embodiments,the redirect response comprises or uses a technique for making a webpageavailable under many URLs. In some embodiments, the linking system usesscripting for redirection. In some embodiments, the linking system usesa refresh meta tag or HTTP refresh header technique for redirection.

In some embodiments, when the user clicks on or activates the shortenedlink 211 the user or browser is taken to an interstitial page of thelinking system, and then using an HTTP redirect page, an intermediatewebsite of the linking system refers the user to the final destinationsite of URL 204. While doing so, the intermediate website may track fromwhich website the user clicked on the short link, stores various userspecific data, and notes any related cookies, or if there are nocookies, stores a new cookie on the user for the future.

A user of one device, such as client device 102, may share the encodedURL 211 with a plurality of users, such as users on client devices 102b-102N. A user or application may share the encoded URL by emailing theencoded URL to a user. A user or application may share the encoded URLby posting or publishing the encoded URL to a web-site. A user orapplication may share the encoded URL by posting, publishing orforwarding the encoded URL to a social networking site, such as but notlimited to LinkedIn or Facebook. A user or application may share theencoded URL by texting the encoded URL. A user or application may sharethe encoded URL by posting or communicating the encoded URL via acommunication tool, such as Skype or Instant Messenger. A user orapplication may share the encoded URL by serving the encoded URL incontent served by a web-site. A user or application may share theencoded URL by serving the encoded URL in an advertisement or impressionopportunity served by an ad server. A user or application may share theencoded URL via the linking system API or app 225, such as via a linkingsystem bookmark applet on a browser. Any user receiving the encoded URLfrom any device may click on or activate the encoded URL to communicatewith the linking system and be directed to the URL decoded from orcorresponding to the encoded URL.

A click tracker 220 may include an application, program, library,process, service, script, task or any type and form of executableinstructions for tracking actions regarding an encoded URL and/ordecoding the encoded URL. In some embodiments, the click trackeridentifies each instance of a user clicking on an encoded URL and tracksthe number of clicks for the URL via the encoded URL in the database230. In some embodiments, the click tracker identifies each instance ofa user clicking on any of a plurality of encoded URLs that correspond toa URL and tracks the number of clicks for the URL via any encoded URL inthe database 230. In some embodiments, the click tracker may identifyand track via the database any temporal information regarding the clickson the encoded URL, such as date and time of the click action 250. Insome embodiments, the click tracker may identify and track via thedatabase any source information regarding the clicks on the encoded URL,such a source internet protocol (IP) address, source port and MachineAccess Control (MAC) identifier of the device from which the userclicked on the encoded URL. In some embodiments, the click tracker mayidentify and track via the database any header, field or otherinformation via any application layer payload, such as the HTTP payloadof the packet(s) carrying the click action or request to decode the URL.In some embodiments, the click tracker may identify and track via thedatabase the HTTP header field of referrer to identify and track the URLor webpage from which the click action or request was referred ororiginated.

A user tracker 215 may include an application, program, library,process, service, script, task or any type and form of executableinstructions for tracking and managing information regarding users ofthe linking system and/or users interacting with encoded and decodedURLs. The user tracker may include an interface, such as a web page, tohave users register as users of the linking system. The user tracker maycollect via registration authentication information of the user, such asa user identifier and a password. The user tracker may identify andcollect information from any type and form of cookie 255. The usertracker may receive the cookie via a request to shorten a URL. Thecookie may be any third-party cookie. The cookie may be a cookiegenerated by, provided by or tracked for the linking system. The usertracker or linking system may insert, modify or provide any data,information or attributes in the cookie for the linking system. The usertracker or linking system may include or provide a cookie 255′ incommunicating the redirect response for a click action that decodes theencored URL. The cookie may comprise information, data or attributesthat identify the user, any user's actions, preferences of the userand/or history of user activity or behavior. The cookie may compriseinformation, data or attributes that identify any click actions. Thecookie may comprise information, data or attributes that identify theURL and/or any encoding and/or decoding of the URL. The cookie maycomprise information, data or attributes of redirection or the redirectresponse by the linking system. The user tracker may identify and trackany user activity in encoding URLs. The user tracker may identify andtrack any user activity in decoding URLs. The user tracker may identifyand track any user activity in sharing encoded URLs. The user trackermay store tracked information, data and attributes to the database.

In some embodiments, the click tracker comprises the user tracker or aportion thereof. In some embodiments, the user tracker comprises theclick tracker or a portion thereof. In some embodiments, a tracker 215or 220 comprises both the click tracker and user tracker. In someembodiments, the user tracker is integrated with, interfaced to orcommunicates with the click tracker. The user tracker and click trackermay be designed and constructed to track and store to the databaseinformation about encoding URLs, decoding URLs and clicks of encodedURLS in association with users connected to the encoding of the URLs,decoding of the URLS and clicking on the encoded URLs.

The database 230 may include an application, program, library, process,service, script, task or any type and form of executable instructionsfor tracking and managing information and data stored by, accessed byand/or used by the linking system or any modules or components thereof.The database may be any type and form of Structured Query Language (SQL)database. The database may be any type and form of object oriented orobject based database. The database may be any type and form ofin-memory or real-time memory database. The database may comprise anytype and form of graphical database. The database may comprise any typeand form of data warehousing and/or analytical database. The databasemay comprise any type and form of multi-dimensional database. Thedatabase may store any data and information from any of the functions,operations, systems and methods described herein.

A click analyzer 235 may include an application, program, library,process, service, script, task or any type and form of executableinstructions for analyzing, searching and/or reporting any of theinformation, data and metrics stored by the linking system in thedatabase 230. The click analyzer may include any type and form of onlineanalytical processing (OLAP). The click analyzer may analyze click anduser data stored in the database to determine a number of clicks to aURL per encoding of the URL. The click analyzer may analyze click anduser data stored in the database to determine a number of clicks to aURL for all encodings of the URL across a plurality of users. The clickanalyzer may analyze click and user data stored in the database todetermine a location of users who clicked on an encoded URL, such aswhat countries the clicks originated from. The click analyzer mayanalyze click and user data stored in the database to determine thedifferent referring sites from which users clicked on an encoded URL.The click analyzer may analyze click and user data stored in thedatabase to determine the different types of clients or clientapplications from which users clicked on an encoded URL. The clickanalyzer may analyze click and user data stored in the database todetermine a number of clicks over a predetermined time period or afrequency of clicks. The click analyzer may analyze click and user datastored in the database to determine a number of conversations acrossdifferent social media networks regarding or in connection with anencoded URL. The click analyzer may provide any data, information and/oranalysis in a graphical format, such as any type and form of statisticalcharts or diagrams.

A plurality of users may click on 250 the same encoded URL 211. Each ofthese users may also click on a plurality of different encoded URLs tothe same URL or to different URLs. The plurality of click actions 250may generate and/or provide data that is tracked and stored via thelinking system. The set of data resulting from a click action and/ordata associated with the click and/or collected, tracked, and analyzedeither statically or in real-time by the linking system may be referredto as a clickstream 250′ or click stream 250′. The click stream mayinclude any data tracked by the user tracker. The click stream mayinclude any data tracked by the click tracker, such as any networktraffic data. The click stream may include any data provided by thebrowser. The click stream may include any data provided via the HTTPrequest. The click stream may include any data analyzed by the clickanalyzer. The click stream may include any data traversing the linkingsystem.

C. Systems and Methods for Scoring Digital Resources

Referring now to FIGS. 3A and 3B, systems and methods for scoring adigital resource, such as content is depicted. These systems and methodsscore content, URLs, domains, phrases or any entity (all of which areexamples of digital resources) based on an expected relevance to anindividual user which may be based on that user's previous engagementwith digital resources. Relevance is from the perspective of a user to adigital resource. By tracking what digital resources a user hasinteracted with and analyzing and classifying those digital resources,these systems and methods generate a score that identifies how similar apresented or identified digital resource is to other digital resourcesthe user has engaged with For example, by tracking what content a userhas clicked on, and by analyzing and classifying that content, thesystems and methods of the present solution, when shown a new set ofcontent, may generate a score determining how similar that content is toother content the user has engaged with. The relevance score produced bythese systems and methods provide an indicator of relevance ofidentified digital resource is to a particular user.

Embodiments of the system of the present solution may take as input: (i)a list of actions associated with a given user (user actions) and (ii) alist of actions associated with entities (global actions). For useractions, the system may identify users via a cookie or a set of cookies.For example, a typical user may have actions associated with a set ofcookies, all associated with an individual user. In some embodiments, acookie-classifier can be used to associate multiple cookies with aspecific individual. A list of actions associated with users may beobtained outside of cookies, such as importing web logs or user activitydatabases. Global actions may include sharing a link, or sharing a pieceof content. Typical examples may include a user forwarding a link viaemail to another user (e.g., the link is the entity, the forwarding isthe action). In another example, a global action may include sharing aparagraph of content on a social media platform like a Facebook update(e.g., the update content is the entity, and “shared on Facebook” is theaction). Embodiments of the system of the present solution may use clickstream 250′ data.

In example operation, the system processes the global actions through acontent extractor, which extracts meta-data, phrases, keywords that arespecific to the entity. The system may provide the global actions andextracted content as input to a pattern classifier. The system may alsoprovide user actions to the pattern classifier. The pattern classifiermay classify the global actions, the user actions and the extractedcontent into a plurality of classes that is stored by the system. Thesystem may receive a request to score a digital resource for a specificuser. For example the digital resource may include piece of data, akeyword, phrase, URL, or domain name, and the user may be identified bya user ID such as the user's cookie ID. The system matches the digitalresource and user identifier with the classified data from theclassifier, and the output is a content or relevance score and a datascore. The relevance score identifies how closely the digital resource(e.g., new piece of data) matches the digital resources associated withthat user. The data score estimates or identified how valid therelevance score is. The data score may be based on how much data wasassociated with the intersection of the user action and the globalactions.

Referring now to FIG. 3A, an embodiment of a system for scoring digitalresources 340 is depicted. In brief overview, embodiments of the linkingsystem 120 may receive or obtain user actions 305 and global actions310, such as via a click stream 250′. The linking system may include acontent extractor 315 to identify keywords, meta-data and phrases fromcontent identified by any of the user actions and/or the global actions.The linking system may include a classifier 320 that classifies patternsfrom the user actions, global actions and the keywords, meta-data and/orphrases from the content extractor. A relevance scorer 325 receives arequest to generate a score for a user id 345 and a digital resourceidentifier 342 that identifies a digital resource 240. The relevancescorer matches the user id and digital resource identifier via theclassification data of the classifier and generates a relevance score330 to identify the relevance of the identified digital resource for theidentified user based on what digital resources the user has previouslyinteracted with. The relevance scorer may generate a data score thatidentifies or estimates the validity or quality of the relevance score.

In further details, a digital resource 340 may comprise any type andform of electronic, digital or web based resource (sometimes referred toas an entity or digital entity). The digital resource may be a domainname. The digital resource may be a web-site. The digital resource maybe a URL. The digital resource may be an encoded URL. The digitalresource may be a web page. The digital resource may be a keyword indigital content. The digital resource may be a phrase in electroniccontent. The digital resource may be meta-data in or of digital content.The digital resource may be digital content. The digital resource may bea file. The digital resource may be a portion, copy or snippet ofdigital content or a file. The digital resource may be an advertisement.The digital resource may be a text or SMS message. The digital resourcemay be an email. The digital resource may be an IM or chat message. Thedigital resource may be an IP based audio and/or video communication.The digital resource may be a posting on a web-site. The digitalresource may be a discussion or conversation, or portion thereof, on aweb-site. The digital resource may be a message, posting or content on asocial networking site. The digital resource may be digital audio, suchas an audio file. The digital resource may be music or music file. Thedigital resource may be a video. The digital resource may be an image.The digital resource may be a graphic file. The digital resource may bean application, program, library, program or script. The digitalresource may be a device.

The digital resource identifier or id 342 may comprise any type and formof identifier associated with, corresponding to or that otherwiseidentifies a digital resource. The digital resource id may be a uniqueidentifier. The digital resource id may be a hash code. The digitalresource id may be a hash of the digital resource. The digital resourceid may be a name of the digital resource. The digital resource id may bea URL of or to the digital resource. The digital resource id may be amemory location of the digital resource. The digital resource id may bea storage location of the digital resource. The digital resource id maybe a name of a file corresponding to the digital resource. The digitalresource id may be the digital resource itself, such as a URLidentifying the digital resource of the URL itself or a domain nameidentifying the digital resource of a domain.

The user actions 305 may comprise any type and form of actions of auser. In some embodiments, the user actions may comprise any actions ofa user to interact or interacting with a digital resource. the useractions may comprise any actions of a user to request, access, obtain,view, print, edit, user or otherwise process a digital resource. In someembodiments, a user action comprises registering and/or logging in tothe linking system. In some embodiments, a user action comprisesregistering and/or logging in to a web-site. In some embodiments, a useraction comprises encoding a URL or requesting the linking system toencode the URL. In some embodiments, a user action comprises clicking oractivating by a user a URL. In some embodiments, a user action comprisesclicking or activating by a user an encoded URL. In some embodiments, auser action comprises requesting a browser to go to a URL or web-page.In some embodiments, a user action comprises a pointer over or mouseover of a keyword, phrase or URL on a web page. In some embodiments, auser action comprises a selection of a keyword, phrase or URL on a webpage. In some embodiments, a user action comprises a traversal betweenURLS or web-links, such as clicking on a hyperlink of one page to get toanother page of a web-site. In some embodiments, a user action compriseslaunching, executing or using a browser, such as a browser of a certaintype. In some embodiments, user actions comprise a history of activityof a user on a computer, browser or web-site, including date and time ofsuch activity. In some embodiments, user actions comprise a log or fileof activity of a user on a computer, browser or web-site, including dateand time of such activity. User actions may comprise the click stream orany portion thereof.

The global actions 310 may include any type and form of actionsregarding sharing, forwarding, propagating or otherwise providing adigital resource to another entity, user or another digital resource. Insome embodiments, a global actions comprises a user electronicallycommunicating a digital resource or digital resource identifier to anentity, user or digital resource. In some embodiments, a global actioncomprises a user emailing a digital resource or digital resourceidentifier. In some embodiments, a global action comprises a usertexting or sending an SMS message comprising a digital resource ordigital resource identifier. In some embodiments, a global actioncomprises a user instant messaging a digital resource or digitalresource identifier. In some embodiments, a global action comprises auser posting a digital resource to a web-site. In some embodiments, aglobal action comprises a user posting, sharing or provide a digitalresource or digital resource identifier via, in or to a socialnetworking site. In some embodiments, a global action comprises a usercutting, copying and/or pasting a digital resource to another digitalresource, such as copying and pasting a portion of content of a web-siteto a social networking web-site. In some embodiments, a global actioncomprises a user forwarding, sharing or providing an encoded URL toanother user, entity or digital resource. In some embodiments, a globalaction comprises a user transforming or processing a digital resource inone form or format to a digital resource in another form or format.Global actions may comprise the click stream or any portion thereof.

The content extractor 315 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions for identifying, extracting, or processing keywords,phrases and data from content. The content extractor may be designed andconstructed to identify, obtain or retrieve content from a digitalresource, such as a web page identified by a URL. The content extractormay be designed and constructed to identify, determine, and/or extractkeywords and/or phrases associated with, corresponding to from contentof a digital resource. The content extractor may identify, determine,and/or extract keywords and/or phrases corresponding to or matching apredetermined list or enumerations of keywords and/or phrases. Thecontent extractor may identify and retrieve content for a URL decodedfrom an encoded URL. The content extractor may identify and retrievekeywords and/or phrases from content identified by a URL decoded from anencoded URL. The content extractor may identify and retrieve content,and/or keywords and/or phrases, from a predetermined portion of adigital resource. The content extractor may identify and retrieve textareas of a digital resource. The content extractor may identify andretrieve keywords and/or phrases from text areas of the digitalresource. The content extractor may identify and retrieve meta-data fromor about a digital resource. The content extractor may identify andretrieve keywords and/or phrases from the meta-data. In someembodiments, the content extractor may identify and retrieve content,and/or keywords and/or phrases, from user selected or defined portionsof the digital resource. In some embodiments, the content extractor mayidentify a keyword in the digital resource. In some embodiments, thecontent extractor may identify a phrase in the digital resource. Thecontent extractor may identify one or more URLs on a web page. In someembodiments, the content extractor may identify URLs from predeterminedportions of the page. In some embodiments, the content extractor mayidentify URLs from user selected or defined portions of a page. Thecontent extractor may retrieve content from the identified one or moreURLs and identify or retrieve keywords and/or phrases from such content.

In view of any media content, such as video and/or audio files, thecontent extractor may be designed and constructed to analyze the contentof such media to determine any text, phrases or meta-data containedtherein or related thereto. In some embodiments, the content extractormay include any type and form of voice or audio recognition technologyto identify audio content in the media, such as spoken words, music orsounds. In some embodiments, the content extractor may convert any ofthe audio content into corresponding text. In some embodiments, thecontent extractor may identify text or phrases from the text convertedfrom and corresponding to the audio content. In some embodiments, thecontent extractor may include any type and form of video processing andanalysis technology that identifies persons, location, objects or thingsin a video. From such video processing and analysis, the contentextractor may provide a description, such as in text format, of thesubject matter of or the persons, location, objects or things in thevideo. In some embodiments, the content extractor may identify text orphrases from the description determines from and corresponding to thevideo content.

The classifier 320 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions for performing or providing pattern classification of a setof data. The classifier may use any type and form of classificationscheme or algorithm to identify a sub-population, class or category towhich a new observation or item belongs in which the identity of thesub-population, class or category for the new observation or item is notknown. The classifier may perform pattern recognition, which is theassignment of some sort of output value (or label) to a given inputvalue (or instance), according to some specific algorithm. Via patternrecognition, the classifier attempts to assign each input value to oneof a given set of classes (for example, determine whether a given emailis “spam” or “non-spam”). Classifier may classify based on a training,learning or established set of data containing observations or itemswith a known sub-population, class or category. The classifier maycomprise any type of classifier, such as a neural network, supportvector machines, k-nearest neighbors, Gaussian mixture model, Gaussian,naive Bayes, decision tree and/or Radial Basis Function (RBF)classifier.

The classifier may take as input any one or more of the following: adigital resource, a digital identifier, user actions and global actions.The classifier may take as input any one or more of the keywords and/orphrases identified by the content extractor. The classifier may classifythis input to assign or designate a sub-population, class or category toeach input or sets of input. The classifier may perform thisclassification on a per user basis. The classifier make take inputassociated with a user, such as user actions and global actions for aparticular user, and classify such input into classes or categories andstore such classification in association with the user. The classifiermake take input associated with a user, such as user actions and globalactions for a particular user, identify keywords from the input andclassify the keywords into classes or categories and store suchclassification in association with the user.

The classifier may classify the input into categories or classes basedon keywords and/or phrases identified from the digital resource and/orfrom content of, identified by or associated with the digital resource.The classifier may classify the input into categories or classes basedon subject matter. The classifier may classify the input into categoriesor classes based on topics. The classifier may classify the input intocategories or classes based on context. The classifier may classify theinput into categories or classes based on areas of interest. Theclassifier may classify the input into categories or classes based onpreferences of the user. The classifier may classify the input intocategories or classes based on favorites of the user. The classifier mayclassify the input into categories or classes based on an affinity oraffinities of the user. The classifier may classify the input intocategories or classes based on type of digital resource. The classifiermay classify the input into categories or classes based on source of thedigital resource. The classifier may classify the input into categoriesor classes based on digital resource identifier. The classifier mayclassify the input into categories or classes based on type of action.The classifier may classify the input into categories or classes basedon encoded URLs. The classifier may classify the input into categoriesor classes based on decoding URLs. The classifier may classify the inputinto categories or classes based on URLs. The classifier may classifythe input into categories or classes based on domain names. Theclassifier may classify the input into categories or classes based ontemporal information, such as time and/or date of interaction with thedigital resource.

The classifier may classify the input based on a cross-section, matchingor association of user actions to global actions. The classifier mayidentify those URLs encoded by a user and the encoded URLs clicked on bythe same user but encoded from and shared by other users. The classifiermay match those URLs encoded by a user to those global actions in whichthe user's encoded URLS were shared. The classifier may classify theseURLs into categories or classes. The classifier may classify keywordsand/or phrases from these URLs or content associated therewith intocategories or classes. The classifier may classify into categories orclasses based time and/or date of interaction with encoded URLs.

The relevance scorer 325 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions for generating and/or providing a score for a digitalresource. The relevance scorer and/or classifier may perform any typeand form of statistical analysis or modeling of the classification data.The relevance scorer may perform any type and form of fuzzy logicmatching to match an input, such as a digital resource identifier, tothe classification data and/or statistical analysis or model to generateor provide a relevance score 330. The relevance scorer may receive orprocess as input a digital resource identifier 342 and a user identifier345. Based on this input, the relevance scorer may determine howrelevant the digital resource identified by the digital resourceidentifier is to the user identified by the user id based on that user'sprevious interaction with digital resources as may be represented orreflected in the classification data and/or statistical analysis ormodel.

The relevance scorer may classify the digital resource identified by thedigital resource identifier into an existing class or category of theclassification data and/or statistical analysis or model for the userspecified by the user id. In some embodiments, the relevance scorerdetermines whether the classification of the digital resource identifiedby the digital resource identifier fits into an existing class/categoryor a new class/category of the classification data and/or statisticalanalysis or model for the user specified by the user id. In someembodiments, the relevance scorer determines a number of classes orcategories into which the digital resource identified by the digitalresource identifier may be classified. In some embodiments, therelevance scorer determines a number of other digital resourcespreviously classified in the class or category into which the digitalresource identified by the digital resource identifier may beclassified. In some embodiments, the relevance scorer determinestemporal information (times and dates of interaction, velocity of rateof interaction, etc.) for digital resources previously classified in theclass or category into which the digital resource identified by thedigital resource identifier may be classified. For example, in someembodiments, the relevance scorer may classify or match the digitalresource identified by the digital resource identifier based onclassification data within a predetermined time period. In someembodiments, the relevance scorer may generate, extract or identify oneor more keywords for or from the digital resource identified by thedigital resource identifier and match these one or more keywords tokeywords in the classification data and/or statistical analysis or modelfor the user specified by the user id.

The relevance scorer may generate or provide a relevance score 330responsive to an analysis or classification of digital resourceidentified by the digital resource identifier to the classification dataof the user identified by the user id. The relevance scorer may generatethe relevance score by any statistical calculation of the classificationof the digital resource identified by the digital resource identifierinto or using the classification data of the user specified by the userid. The relevance scorer may generate the relevance score by anytemporal weighting of temporal information of the classification data ofthe user specified by the user id. The relevance scorer may generate therelevance score using any of the scoring methods and techniquesdescribed elsewhere herein.

The relevance score 330 may comprise a value that provides an indicationof or otherwise identifies how relevant the digital resource identifiedby the digital resource identifier is to the user specified by the userid. The relevance score may be generated for or on an absolute orrelative scale. The relevance score may be generated for or normalizedto a predetermined relevance range, such as for example −100 to 100, 0to 100 or X to Y.

The relevance scorer may generate or provide a data score responsive to,in connection with or in conjunction to generating or providing therelevance score 330. The relevance scorer may generate or provide a datascore by any statistical calculation of an amount of cross-section ormatching in the classification data between user actions and globalactions associated with the user. The relevance scorer may generate orprovide a data score based on an amount or volume of data for the userin the classification data. The relevance scorer may generate or providea data score based on an amount or volume of data of user actions forthe user in the classification data. The relevance scorer may generateor provide a data score based on an amount or volume of data of globalactions for the user in the classification data. The relevance scorermay generate or provide a data score based on temporal qualities of thedata for the user in the classification data.

The data score 335 may comprise a value that provides an indication ofor otherwise identifies a quality of data supporting or underlying therelevance score. The data score 335 may comprise a value that providesan indication of or otherwise identifies a validity of the relevancescore based on a volume or quality of the classification data. The datascore 335 may comprise a value that provides an indication of orotherwise identifies a validity of the relevance score based on atemporal quality of the classification data. The data score 335 maycomprise a value that provides an indication of or otherwise identifiesan amount of cross-section or matching between user actions and globalactions associated with the user. The data score may be generated for oron an absolute or relative scale. The data score may be generated for ornormalized to a predetermined range, such as for example −100 to 100, 0to 100 or X to Y.

The linking system and/or relevance scorer may identify the user via theuser identifier. The user identifier 345 may comprise any type and formof identification of a user, such a name, alias, an account name, alogin name, or email address. The user identifier may be user identifierfor the user for using or accessing the linking system. The useridentifier may be user identifier for the user for using or accessing asocial networking site. The user identifier may be user identifier forthe user for using or accessing a third-party or partner web-site. Theuser identifier may be based on a cookie. The user identifier may bestored in a cookie. The linking system and/or relevance scorer mayidentify the user via one or more cookies.

Referring now to FIG. 3B, an embodiment of a method for scoring adigital resource 340 is depicted. In brief overview, at step 350, thelinking system receives user actions and at step 355, the linking systemreceives global actions. At step 360, a content extractor extracts oridentified keywords from content identified by or associated with adigital resource identified in any of the user actions and/or globalactions. At step 365, the classifier performs classification on theinput of user actions, global actions and keywords identified therein.At step 370, a relevance scorer receives a user identifier and a digitalresource identifier and generates a relevance score and data score.

In further details, at step 350, a server, such as the linking system,receives user actions from a plurality of users. The server may receiveuser actions via click streams 250′. The server may receive user actionsvia or comprising a user interacting, user or accessing a digitalresource. The server may receive users actions from the digitalresource. The server may receive a user action via a user requesting toencode an URL. The server may receive a user action via a user clickingon an encoded URL. The server may receive a user action via a request todecode an encoded URL. The server may receive and track user actionsover any period of time. The server may receive a user action via aclient linking system API or application 225. The server may receive oneor more user actions via a log file or activity log from an application,system, device or server. The click tracker may identify clicks of useron encoded URLs. The user tracker may identify the user who clicked theencoded URL. The user tracker may identify the user who encoded theencoded URL. The server may store the user actions associated with orattributed to each user into a database 230 and associated with oridentifiable via a user identifier.

At step 355, a server, such as the linking system receives globalactions. The server may receive global actions via a plurality of clickstreams 250′. The server may receive global actions via a user clickingon an encoded URL, such as encoded URL shared on a social networkingsite. The server may receive global actions via or comprising a usersharing a digital resource. The server may receive global actions fromthe digital resource. The server may receive one or more global actionsvia one or more log files or activity logs from an application, system,device or server. The user tracker may identify the user who shared theencoded URL. The click tracker may identify clicks of users on encodedURLs that have been shared. The user tracker may identify the user whoclicked the shared encoded URL. The server may store the global actionsassociated with or attributed to each user into a database 430 andassociated with or identifiable via a user identifier.

In some embodiments, steps 350 and 355 are provided or performed as asingle step. In some embodiments, steps 350 and 355 are provided orperformed in conjunction during the same step or sets of steps. Forexample, in some embodiments, the server may receive user actions andglobal actions via click streams received by the server. The server maytrack, manage and store any user and global actions on a per user basisin a database.

At step 360, a content extractor may identify keywords and/or phrasesfrom a digital resource, content identified by the digital resource orcontent otherwise associated with or corresponding to the digitalresource. The content extractor may operate responsive to receipt ofuser actions and/or global action. As the server receives a clickstream, the content extractor may identifier keywords or phrases fromthe click stream. In some embodiments, the context extractor obtains orfetches content corresponding to the digital resource. In someembodiments, the content extractor uses a predetermined list of phrasesand/or keywords to identify those phrases and/or keywords in the digitalresource, content identified by the digital resource or contentotherwise associated with or corresponding to the digital resource. Insome embodiments, the content extractor identifies keywords and/orphrases from predetermined locations or portions of a digital resourceor content associated with the digital resource. In some embodiments,the content extractor identifies keywords and/or phrases from useractions. In some embodiments, the content extractor identifies keywordsand/or phrases from global actions. In some embodiments, the contentextractor identifies keywords and/or phrases on a per user basis or foreach user.

At step 365, a classifier performs classification or otherwiseclassifies the keywords and/or phrases. The classifier may operateresponsive to the content extractor. As the content extractor identifiedkeywords or phrases, the classifier may receive these keywords orphrases and perform classification. The classifier may classify anycombination of keywords and/or phrases from the digital resourceidentifier, digital resource, content identified by the digital resourceor content otherwise associated with or corresponding to the digitalresource, user actions and global actions. The classifier may storeclassification data for each user. The classification data may representa classification of the user's interactions with digital resources intocategories or classes. The classifier may generate classification datathat represent a classification of the user's interactions with thedigital resources based on subject matter, interests or topics.

At step 370, the relevance scorer may receive a digital resourceidentifier and a user identifier. The relevance scorer may receive arequest to provide a relevance score for a digital resource or entityidentified by the digital resource identifier for a user identified bythe user identifier. Responsive to the request for a relevance score orreceipt of a digital resource identifier and a user identifier, therelevance scorer may generate, communicate or otherwise provide arelevance or content score. The relevance scorer may receive a requestto provide a data score for a digital resource or entity identified bythe digital resource identifier for a user identified by the useridentifier. Responsive to the request for a data score or receipt of adigital resource identifier and a user identifier, the relevance scorermay generate, communicate or otherwise provide a data score. In someembodiments, responsive to generating, communicating or providing arelevance or content score, the relevance score may also generate,communicate or otherwise provide a data score.

The relevance scorer may receive a digital resource identifier and auser identifier or a request from any application, system or server. Insome embodiments, a third party web site serving content may transmitthe request and/or digital resource identifier and a user identifier tothe linking system or relevance scorer. In some embodiments, an adserver serving advertisement or matching content to impressionopportunities may transmit the request and/or digital resourceidentifier and a user identifier to the linking system or relevancescorer. In some embodiments, a client application may transmit therequest and/or digital resource identifier and a user identifier to thelinking system or relevance scorer.

D. Systems and Methods for Providing Search Results Based on UserInteraction with Content

In some embodiments, the present disclosure is directed to systems andmethods for providing relevant real-time or static search results. Thesereal-time or static search results may be provided based on useractivity and/or engagement measurement from users with respect toparticular content and websites. In some embodiments, the linking system120 may provide or support interactive search based on global engagementpatterns of users, for example, across large internet platforms oracross multiple platforms. The search results may rank available contentbased on popularity of the content, which may be determined by uservisits and/or referring websites. These results may be determined basedon actual interactions between people and content. Such methods, whenincorporated into searches, may yield significant improvements overthose using typical link crawling and ranking based on links that resideon a page.

Referring to FIG. 4A, one embodiment of a system for providing searchresults based on user interaction with content is depicted. In briefoverview, the system includes a linking system 120 that incorporates arelevance system 405 for facilitating a search request. The relevancesystem 405 may receive a click stream 250′, as well as keywords orsearch terms 447 from a search request. In some embodiments, therelevance system 405 includes one or more of the following modules:distribution scorer 410, engagement scorer 415, frequency normalizer420, source weighting module 425, user weighting module 430, time decayfunction 435 and search relevance scorer 440. The relevance system 405may determine a relevancy score for each document identified in aclickstream, for ranking documents during a search.

In certain embodiments, the linking system 120, via a click tracker, maycollect, track or otherwise monitor information about links or contentaccessed by one or more users. The linking system 120 or the clicktracker may determine the content that users selected via encoded links,and may analyze the selected content to facilitate interactive search.By way of illustration, and in one embodiment, the linking system 120may prioritize content and/or links based on one or more of: (i) theidentity or other information of the user clicking on the link, (ii) theidentity or other information of the website providing the content orlink to the content, and (iii) the timing and/or number of usersaccessing the content via a certain link. In certain embodiments, thisprioritization may be determined or computed based on one or moremathematical and/or computer techniques, which may be custom orproprietary to the linking system 120. The linking system 120 mayestablish, maintain and/or update a database of content or links basedon the above determination, analysis and/or prioritization.

The database 230 may include any information or data associated with aclick on an encoded link. The database 230 may include data collected,tracked, and analyzed either statically or in real-time, and associatedwith the click. The linking system, via the click tracker, may analyzeand/or parse a clickstream for data to extract and store in the database230. In some embodiments, the database 230 may store data collected,tracked, and analyzed by the linking system 120 in response to amouse-over, copy or paste of an encoded link. The linking system maystore any portion of the collected data in a record of the database. Insome embodiments, a stored record may correspond to a click or otheruser action. The record may include any user and/or traffic dataprovided by a web browser, such as those described above in connectionwith FIGS. 2, 3A and 3B.

In some embodiments, a search engine or system is supported by thelinking system 120 in real time or substantially in real time. Thesearch system may change or update the relevancy of content for a useras that user and/or other users click on or navigate links (e.g.,encoded links) on a page. The relevancy of a content may be determinedin relation to keywords or search terms, user activity and/or activityfrom various websites. The search system may communicate with or rely onthe linking system 120 to process the clickstreams in real time orsubstantially real time. In some embodiments, the linking systemincludes a clickstream processing module, which may be referred to as arelevance system 405 or omniflector. The linking system 120 may processor decode a clickstream in real time, and may dissect, organize, bufferand/or store the processed information to the database 230.

In some embodiments, the database 230 may be referred to as aredistribution database or a sharded redis cluster database. Thedatabase 230 may comprise one or more sharded redis cluster databases,e.g., in a storage area network (SAN). In certain embodiments, therelevance system 405 may receive and/or process a portion of theclickstream to obtain a social score for each content or document(hereafter sometimes generally referred to as “document” or “content”).The relevance system 405 may perform calculations on a portion of thedecoded stream, to generate the social score and/or other data. Thelinking system 120 may store the social score and/or other data to thedatabase 230. In some embodiments, the linking system, e.g., via therelevance system 405, may receive or access a portion of the stored orbuffered information. The linking system 120 may use this information togenerate, create, calculate or otherwise determine a social score foreach document.

In some embodiments, a social score indicates, describes or representshow popular or in demand a certain document is. In some embodiments, asocial score is a weight to be applied to the relevancy scoring. Asocial score may be determined based on the number of times a documentis “clicked” or accessed, and/or how widely the document is being sharedor referred (e.g., a corresponding encoded link is forwarded or copied).The social score may be tempered by, or incorporate, a decay function toallow old content to decay, and new content to gain traction, prominenceor relevance in searches. The linking system 120 or relevance system 405may include a decode processor, which may comprise or execute anyapplication, algorithm, program, library, process, service, script, taskor any type and form of instructions for determining social scores. Insome embodiments, the decode processor includes a chip or a chipset,e.g., a CPU, an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or any other hardware fordetermining the social score.

By way of illustration, and not intended to be limiting in any way, thesocial score may be any real number or integer ranging between 1 and 100(or any other range). A maximum of 80 points for example, may be mapped,derived, or contributed from a raw social score. A maximum of 10 points,for example, may be mapped, derived, or contributed from a count of thetotal number of social referrers. A maximum of 10 points, for example,may be mapped, derived, or contributed from a count of the total numberof other referrers. The raw score may indicate the reach of a web siteor a document online, and may include a number of components, includinga distribution score, an engagement score, etc. The raw score may haveany value larger than one (or zero, in some embodiments). A raw scorefalling within certain predefined range may be mapped to a value thatmakes up a portion (e.g., 80%) of the social score. The total number ofsocial and/or other referrers may similarly yield component value(s)that contribute to the final value of the social score. By way ofexample, one embodiment of a pseudo code or algorithmic description ofthe determination of a social score is depicted below:

a=raw social score [e.g., weighted number of clicks, weight may indicatethe reach of a site on the internet]

b=total count of social referrers

c=total count of other (e.g., non-social) referrers

d=mapped social score [a: 1-400->buckets 1-70; a: 401-2000->buckets71-80, >2000=80]

e=mapped social referrers [b*3, may be bounded between 0 and 10]

f=mapped other referrers [c/2, may be bounded between 0 and 10].

In some embodiments, the raw social score may contribute the largestcomponent of the social score. However, various different attributionweights may be implemented in various embodiments, and applied to eachof the social score components. In one embodiment, the linking system120 (e.g., via a source weighting module 425) may assign a weight (e.g.,ranging between 1 and 10) to certain websites (e.g., top sites) ordocuments in accordance with the reach they have on the web. Sites withhigh or broad reach, such as Facebook and Twitter, may be assigned aweight of 10 or some large value. Lower reaching sites, such asXing.com, may be assigned a 1 or some small value. In some embodiments,when a social click is identified or detected, the raw social score fora corresponding document is adjusted according to the appropriate,assigned weight. By way of example, a click from Facebook may be worth10 times as much as a click from Xing, and correspondingly representedby their assigned weights.

The raw social score of a document may continue to increase in value asthe document's link receives more clicks. In one illustrativeembodiment, the raw score may be mapped to a number between 1 and 80,e.g., with the bottom 70 points mapped to lower raw score ranges, andthe last 10 points mapped to higher raw score ranges. In someembodiments, the source weighting module 425 may receive source weighttraining data 427 to generate an initial social score, or to test thesocial scoring system. The source weight training data 427 may includeweights to assign to certain sites, and these weights may be adjusted orupdated as sites are ranked and re-ranked. In some embodiments, thesource weight training data 427 may include clickstreams (e.g.,simulated, historical, or received in real time) for evaluating sourceweighting and social scores.

In some embodiments, the relevance system 405 identifies users thattriggered the clickstreams and includes a user weighting module 430configured to weight the social score based on the identified users. Theuser weighting module 430 may assign specific weights to particularusers, such as those identified as influencers (e.g., showingsignificant social networking reach in sharing content, etc.) and/orpower users (e.g., performing significant number of searches, which mayinfluence search relevancy in a positive or negative way for otherusers). By way of example, the user weighting module 430 may measure thevalue of particular users (e.g., a leading social networker on shoefashion trends) on specific keywords, search terms or search strings(e.g., “lace up boots”, “tasseled leather pumps”), and may categorizesuch users as influencers. The user weighting module 430 may weightsearch results based on identifier influencers, for example boostingsearch results for content that is either encoded or decoded byinfluencers. In certain embodiments, the user weighting module 430assigns weights to clicks associated with certain user data orcharacteristics (e.g., gender, age group, or users whose online historyinclude visits to websites catering to specific subject matter).

In certain embodiments, the total number or count of social referrersrepresents the total number of unique referring domains for a certaindocument or content. These referring domains may be social websites oronline social networks. The total number of other referrers mayrepresent the total number of unique referring domains that are notsocial sites, for a certain document or content. In the above example,the total number of social referrers may be multiplied by three, and theresult bounded between 0 and 10. The linking system 120 may divide thetotal number of other referrers by 2 (or some other number), and boundthe result between 0 and 10. Either of these referrer counts may beadjusted by some other predetermined multiplication (or division) factorbefore contributing to the social score. In addition, either of thesereferrer counts may be capped or bounded by other ranges (e.g., 0-100)after adjustment.

Any of the above information may be used to determine the social scoreand/or to determine which documents get indexed within search lists.Once a document is indexed, the relevancy score for a document isdetermined by a relevance system of the linking system 120 using acombination of word relevancy and the document's social score. Thesocial score may indicate that a document is relatively more important,or is more relevant to a search if the document has been accessed byusers and/or from domains (e.g., via encoded links) that have a lot ofinfluence or reach on the web. It may be expected that documentsaccessed from popular sites may be more popular and relevant to a userin a search. The linking system 120 may index each document identifiedin a clickstream. The linking system 120 may index or store the documentin the database 230 with or against a corresponding social score. Thissocial score of a document may be updated in real time, according to aschedule, or in response to a triggering event (e.g., a search). Incertain embodiments, the relevance system 405 and/or search system mayaccess, compare and/or use the social score of a document during asearch.

In some embodiments, the social score of a document is determined basedon social clicks that access the document via encoded links. In otherembodiments, the social score may incorporate one or more types ofclicks, which may include non-social clicks. In some embodiments, socialclicks include clicks on document links from identified top websites. Insome of these embodiments, social clicks include clicks on links fromtop social sites. The linking system may identify, maintain or track anumber of top sites, e.g., 1000 top sites, which may be provided bypartners, market research providers (e.g., Doubleclick), analytics (e.g.BlueKai and Exelate) or search providers (e.g., Yahoo, Google and Bing).In some embodiments, the top sites may comprise, exclusively ornon-exclusively, major social sites (e.g., Facebook, Twitter, Google+,LinkedIn, etc). The linking system 120 may determine that a clickreferred, redirected or consummated from one of these top sites ordomains may be considered a social click. As an illustration of therelative importance or relevance of social clicks, consider thefollowing: a linking system that focuses on social clicks from the top1000 sites may be able to index less than 1% of the domains beingclicked, but such clicks may account for over 38% of all online clicks.Thus, social clicks may be useful for determining social scores foronline content. In certain embodiments, the linking system 120 maydynamically monitor click counts from domains and determine top sitesbased on the distribution of click counts. The linking system maydynamically identify top sites, e.g., in lieu of a static list of topsites provided by a third party.

In certain embodiments, the linking system may classify or group two ormore clicks on a social site for the same document, occurring within acertain time period (e.g., 2 or 5 minutes), as a social click. In someembodiments, the linking system may group multiple clicks from othertypes of websites as a single social click. Documents identified by asocial click may be included in a directory, database, search list,index or application programming interface to index the document forsearch. In some embodiments, such documents are placed in apublisher/subscriber (pubsub) index or stream that can be pulled duringsearches. A search engine may pull the social clicks pubsub stream andmay continuously index global hashes within the pubsub. Documents thatare popular may tend to be clicked over and over again. Such documentsmay get indexed again and again corresponding to the clicks. Asdocuments' links are clicked, the linking system 120 may update thedocuments' social scores. As the documents get re-indexed, theircorresponding social scores may be higher, resulting in higher rankingswithin search results.

In certain embodiments, the number of social clicks may contribute to asocial score in a linear or non-linear fashion. For example, a socialscore of a document may be configured such that it is directlyproportional to the number of social clicks detected. In some otherembodiments, the first clicks (click number 1-80) from a social websitemay be weighted more than later clicks (click number 81 to 100) indetermining a social score. In one embodiment, and by way ofillustration, the number of social clicks may be represented as follows:

${sc}_{i,j} = {{\sum\limits_{1,80}\frac{s(i)}{10}} + {\sum\limits_{81,100}\frac{s(i)}{100}}}$

In some embodiments, s(i) may represent the click count for term iwithin the respective click count ranges for document j. Additionalclicks (e.g., beyond 100) may be weighted less or differently, ordisregarded after a certain threshold (e.g., 100 clicks). In certainembodiments, the number of social clicks, sc_(i,j), may be referred toas an engagement score. An engagement scorer 415 of the linking systemmay rank, score or rate content based on how many clicks are received.An engagement scorer 415 may include or execute any application,program, library, process, service, script, task or any type and form ofexecutable instructions for generating and/or updating an engagementscore. The engagement scorer 415 may apply the above function, or someother nonlinear function, such that initial clicks are weighted morethan later clicks. The engagement scorer 415 may emphasize initialclicks so that new content may promoted over older content with the samefrequency of clicks. The linking system 120 may, for example, promotenew content by leveraging on a time decay function in conjunction withthe engagement score. The linking system 120 may include or incorporatethe engagement score in a social score.

As described, the linking system 120 may calculate the social score foreach document, and may store the social score in an index. The socialscore may be decayed, reduced or de-emphasized in time for use insearches, e.g., from the time the document is first indexed. A socialscore may decay down to half of its original score over a configuredperiod of time, such as 72 or 168 hours. In some embodiments, a socialscore may be decayed as a function of e^(x) (e.g., f(e^(x))), where xrepresents time. The linking system 120 and/or search system may use thesocial score to determine a relevancy score. The linking system 120 mayinclude a search relevance scorer 440 for determining and/or updatingthe relevancy score of a document. The search relevance scorer 440 mayinclude and/or execute any application, program, library, process,service, script, task or any type and form of executable instructionsfor generating and updating the relevancy score of a document. Thesearch relevance scorer 440 may communicate or interoperate with one ormore modules (e.g., distribution scorer 410, engagement scorer 415,frequency normalizer 420, time decay function 435) in determining and/orupdating a relevancy score. A relevancy score may sometimes be referredto as a search score or a relevance score. The social score may be oneof several factors that determines the relevancy score of a document aspertains to search (e.g., interactive search).

In some embodiments, a relevancy score is based on one or more ofweights, factors or components, for example: (i) Social score (e.g., howpopular a document is), (ii) time decay (e.g., how long a document hasbeen in the index), and (iii) normalized frequency (e.g., how relevantcertain terms are to the text of a document). Other weights, factors orcomponents may include (iv) a distribution score and (v) an engagementscore. In certain embodiments, the social score for a document mayincorporate one or more of the above components, e.g., time decay,distribution score and/or engagement score. For example, the socialscore may be subject to time decay prior to being used by the linkingsystem 120 to determine a relevancy score. In other embodiments, thesocial score may be combined with other factors before being subject totime decay to establish the relevancy score. As discussed, time decaymay incorporate an e^(x) function with respect to time (x), or anylog-linear time decay function. With the time decay function, newdocuments with links that are more recently clicked may have a highersocial and/or relevancy score.

A distribution scorer of the relevance system may calculate or determinethe distribution score or weight for a document. The distribution scorer410 may include any application, program, library, process, service,script, task or any type and form of executable instructions formonitoring or tracking clicks arising from various websites. Thedistribution scorer 410 may determine that a document is ranked higheror more relevant in a search if clicks to access such a document arisefrom a broader set of source web sites. The distribution scorer 410 maydetermine the distribution of clicks (e.g., counts of social clicks ornormal clicks) for the same document across a plurality of sites. Insome embodiments, the distribution scorer 410 may determine the numberof source sites (e.g., specific social sites) that referred clicks for acertain document. In certain embodiments, the distribution score may bereferred as “social distribution”. The distribution score may becalculated, expressed or represented in one embodiment by the followingformula:

${sd}_{ij} = {\max\left( {{\sum\limits_{k}n},{Max\_ SD}} \right)}$

where

$\sum\limits_{k}n$

can represent the number of source sites that have been referring clicksfor a document. In some embodiments,

$\sum\limits_{k}n$

represents the summation of website counts, e.g., over k categories ofclicks or k types of websites. Max_SD may be an upper bound or cap forthe distribution score (e.g., 100 websites). In some embodiments, Max_SDis optional, and may be set to 0. Where Max_SD is specified, thedistribution score may take the larger of Max_SD or the number of sourcesites referring clicks for the document.

In some embodiments, the linking system 120 includes a frequencynormalizer 420. A frequency normalizer 420 may include any application,program, library, process, service, script, task or any type and form ofexecutable instructions for processing a term or word's frequency ofoccurrence in a document. Once enough traffic has been monitored forclickstream activity, the linking system 120 may retrieve a documentidentified in the clickstream, using the corresponding long URL. Thelinking system may extract content that is appropriate for text-based,keyword search. The frequency normalizer 420 may extract key words fromthe extracted content, and may normalize some of these keywords. In someembodiments, the frequency normalizer 420 may insert or store thekeywords and their normalization values into the database 230.

In some embodiments, the frequency normalizer 420 uses a termfrequency—inverse document frequency (tf/idf) ratio or metric to obtainthe normalization values. This metric may be used as a statisticalmeasure to evaluate the importance of a term or word within a document.The importance of a term may increase proportionally to the number oftimes the term appears in the document. The importance of the term maybe offset by the frequency of the word within a superset of documents(e.g., corpus) comprising the document. The linking system may use thetf/idf metric to score or rank a document's relevance in a given searchquery. The linking system may use the tf/idf metric to normalize ordiminish the weight of terms (e.g., “the”, “of”) that occur veryfrequently in the corpus and increase or normalize the weight of terms(e.g., “hibernation”, “omnibus”) that occur rarely.

In some embodiments, the tf/idf metric comprises a term frequency (tf)and the inverse document frequency (idf) components. The tf componentmay represent the occurrence count of a term in a document. The tfcomponent may be determined from the number of times a given termappears in that document, normalized to prevent a bias towards longerdocuments to give a measure of the importance of the term i within theparticular document j. For example, in a document containing 100 wordswherein the word brown appears 3 times, the tf value for brown may bedetermined as ( 3/100)=0.03. In some embodiments, the tf value isexpressed in logarithmic form, e.g., log(0.03). One embodiment of the tfvalue is presented as follows:

${{tf}_{i,j} = \frac{n_{i,j}}{\sum\limits_{k}n_{k,j}}},$

where k may represent the number of distinct terms in the document.

In some embodiments, the idf component is a measure of the generalimportance of a term. It may be obtained by dividing the total number ofdocuments by the number of documents containing the term, and thentaking the logarithm of that quotient. One embodiment of the idf may berepresented as follows:

${{idf}_{i,j} = {\log\left( \frac{D}{\left\{ {{j\text{:}t_{i}} \in d_{j}} \right\} } \right)}},$

where D may represent the corpus or set of all documents, |D| mayrepresent the cardinality of D, or the total number of documents in thecorpus. |{j:t_(i)∈d_(j)}| may represent the number of documents wherethe term t₁ appears. By way of example, if there are 10 milliondocuments and the term brown appears in one thousand of these, theinverse document frequency may be calculated as log(10,000,000/1,000)=4.

The normalization frequency of a document term may be determined as theratio of tf to idf, e.g., tf/idf. In one embodiment, this is representedas:

$\left( {{tf}\text{/}{idf}} \right)_{i,j} = {{{tf}_{i,j}\text{/}{idf}_{i,j}} = \frac{\frac{n_{i,j}}{\sum\limits_{k}n_{k,j}}}{\log\;\frac{D}{\left\{ {{j\text{:}t_{i}} \in d_{j}} \right\} }}}$

In some embodiments, the search relevance scorer 440 may use the tf/idfnormalization frequency to determine the relevancy score. However, insome other embodiments, the search relevance scorer 440 may use adifferent document frequency or a variant of the tf/idf normalizationfrequency. For example and in one embodiment, the search relevancescorer 440 may use a td.idf weight, which may be represented as:

$\left( {{tf} \cdot {idf}} \right)_{i,j} = {{{tf}_{i,j} \times {idf}_{i,j}} = {\frac{n_{i,j}}{\sum\limits_{k}n_{k,j}} \times \log\;\frac{D}{\left\{ {{j\text{:}t_{i}} \in d_{j}} \right\} }}}$

In certain embodiments, the frequency normalizer 420 may compute ordetermine a td-idf value using Lucene scoring methods. A Lucene score,may, for example, be expressed in the following embodiment:

${score} = {{{td} - {{idf}\mspace{14mu}{value}}} = {{coord} \cdot {queryNorm} \cdot {\sum\limits_{i\mspace{14mu}{in}\mspace{14mu} q}\left( {{td} \cdot {idf}^{2} \cdot {Boost} \cdot {norm}} \right)}}}$

where coord may be a score factor based on how many of the query termsare found in the specified document. A document that contains more ofthe query's terms may receive a higher score than another document withfewer query terms. queryNorm may be a normalizing factor for makingscores between queries comparable. Boost may represent a search timeboost of one or more terms in the query as specified in the query text.Boost may be used to access a boost of one or more terms in a multi termquery. norm may include one or more boost and length factors, such asboost factors for specific documents, fields in a document, fieldlength, etc.

In various embodiments, the relevancy scorer 440 may apply differentvariants of Lucene scoring and the td-idf value in determining therelevancy score. In certain embodiments, for example, different fieldsin a document may carry different weights. The relevancy scorer 440 mayconfigure these weights as custom properties for a search, a documentindex, the relevancy scorer 440 and/or frequency normalizer 420 forexample. In one embodiment, the relevancy scorer 440 may confer weightsor boosts to the following content fields: title=5, meta keywords=3,meta description=4, meta site=4, domain=4, url=3, page=1, globalhash=5,h1=7, h2=5, h3=3, h4=2, cities=3. These may be configured by anadministrator and/or determined based on search activity. In someembodiments, arbitrary weights may be assigned and updated based onsearch activity. By way of illustration, the following is a listing ofconfigured document field boosts:

  <entry key=“boosts.site”>4.0</entry> <entrykey=“boosts.domain”>4.0</entry> <entry key=“boosts.url”>3.0</entry><entry key=“boosts.globalhash”>5.0</entry> <entrykey=“boosts.phrase”>5.0</entry> <entry key=“boosts.title”>5.0</entry><entry key=“boosts.description”>4.0</entry> <entrykey=“boosts.keywords”>3.0</entry> <entry key=“boosts.lang”>2.0</entry><entry key=“boosts.h1”>7.0</entry> <entry key=“boosts.h2”>5.0</entry><entry key=“boosts.h3”>3.0</entry> <entry key=“boosts.h4”>2.0</entry><entry key=“boosts.cities”>3.0</entry>

Certain variants of Lucene scoring may incorporate boosts values foreach document field. For example, Lucene scoring may apply aconfiguration in which text in h1 tags are more important than those inh2 tags and the title field, which may be more important than the domainfield, etc. A document or content may be configured for a relativeboost. In certain embodiments, a document or content may not beconfigured for a boost, although it may incorporate a score or weight inthe Lucene scoring method based on the length of text, number of termsin the document and other factors, for example.

In some embodiments, the search relevancy scorer 440 determines therelevancy or search score with the following computation: time-decayedsocial score*tf-idf value. In another embodiment, the search relevancyscorer 440 may determine the relevancy score using the followingformula:

Σ(decaytime·Πtd−idf·social distributbn score·social score)

where Π may represent a direct or Cartesian product of td-idf values.Yet other embodiments may incorporate any of the components described,e.g., engagement score, user weighting, source weighting, etc. Therelevancy scorer may rank or index documents based on any variant orcombination of relevancy scores, and the highest scoring documents maybe returned first in a search.

By way of illustration, a retrieval mechanism may be used to query thedatabase, apply the relevancy score (or a combination of scoresdescribed above), and provide a set of search results 450 ordered by howrelevant they are in the clickstream. The relevance system 405 mayreceive keywords or search terms 447 from a search and may process theseinto a search relevance query 445. Search terms may include one or moreparameters that identify or define audience or user segments. These oneor more parameters may break down, identify or define users intosub-groups, such as by demographics, communication behavior and mediause. In some embodiments, a search term may identify a geography scopeor limitation, such as limiting the search to users who live in Italy.In some embodiments, a search term may identify a language scope orlimitation, such as limiting the search to users who read content inItalian or interacted or clicked on content in Italian. In someembodiments, a search term may identify an influence rating.

The search relevance query 445 may for example, comprise scores forkeywords, indexes into specific groups of documents and/or informationabout the user. The relevance system 405 may apply the search relevancequery 445 against the indexed documents, which are ranked by theirrelevance scores. By matching document relevance against the query 445,the relevance system 405 may return one or more documents in the searchresults. The system 405 may return search results limited to or based onany of the audience or user segmentation terms. For example, if ageography term of Italy and a language term of English, the system mayreturn search results based on English based content interacted with,encoded or clicked on by users locating in Italy.

Although some components or factors may be generally referred to ordescribed herein as scores or scoring, these components or factors maybe considered weights to be applied to the relevancy calculation oralgorithm. For example, a social score, a distribution score, anengagement score, a frequency normalization and/or a time decay functionmay be considered weights or weighting factors for the relevancy systemand may be applied to weight the relevancy score or provide a weight toother components or inputs of the relevancy system.

Referring to FIG. 4B, one embodiment of a method for providing searchresults based on user interaction with content is depicted. In briefoverview, the method includes receiving, by a server, identification ofa plurality of clicks of encoded uniform resource locator (URL) links(455). The server may identify, for each of the plurality of clicks,data about a user who clicked an encoded URL link and traffic dataassociated with a device from which the user clicked the encoded URLlink (460). The server may store a record for each click of theplurality of clicks, the record comprising data about the user andtraffic data associated with each click (465). The server may determine,based on the records, a relevancy score for each content identified fromdecoding the encoded URL links (470). The server may communicate,responsive to receiving a request to search content based on a keyword,a set of search results based on the keyword and the relevancy score(475).

Referring now to (455), a server may receive an identification of aplurality of clicks of encoded uniform resource locator (URL) links. Theserver may comprise any embodiment of the linking system 120 describedabove in connection with FIGS. 2, 3A and 4A. The server may decode eachof the encoded URL links, for example, as described above in connectionwith FIG. 2. The server may decode an encoded URL link in real time. Theserver may redirect the click to access a document from another URL.

In further details of (460), the server may identify, for each of theplurality of clicks, data about a user who clicked an encoded URL linkand traffic data associated with a device from which the user clickedthe encoded URL link. In some embodiments, the server may identify dataabout the user from a cookie communicated via a click by the user on theencoded URL link. The server may identify, for example, traffic datacomprising one or more of a browser type, a referring web site, a sourceinternet protocol address, a destination internet protocol address, atime instance of a click, a document accessed.

The server may identify any other data about the user and/or trafficdata as described above in connection with FIGS. 2 and 4A. For example,the server may determine if a user is an influencer or meets certaincriteria for increased weighting. The server may assign an appropriateweight to this user's social clicks, e.g., in determining a document'ssocial score. In some embodiments, the server may determine, based ontraffic data, if a number of clicks are social clicks that maycontribute to a social score, or if a click is triggered from a websiteidentified as a top site and/or a social website. The server may assigna greater weight to social clicks arising from a top site and/or asocial website. In certain embodiments, the server may track the numberof corresponding clicks from a document, and the distribution of sourcewebsites that triggered the clicks.

Referring now to (465), the server may store a record for each click ofthe plurality of clicks. The server may store the record in anyembodiment of the database 230 described above in connection with FIGS.2 and 4A. The record may comprise any form of data structure such as adatabase or hash entry, a table item, or any collection of dataassociated with a click, document/content, user and/or source website.The record may comprise data about the user and traffic data associatedwith each click. In some embodiments, the server may store an index of adocument identified by a click or clickstream. The server may store,maintain and update an index of documents to facilitate searches forcontent. The server may, in some embodiments, retrieve and store adocument identified via a clickstream in the database 230. In certainembodiments, the server may determine, rank and store a list of topwebsites based on where the clickstreams arose. The record may includefields or memory space for storing one or more scores, such a socialscore and/or a relevancy score for a document.

Referring now to (470), the server may determine, based on the records,a relevancy score for each content identified from decoding the encodedURL links. The server may determine the relevancy score via one or moremodules, for example, a search relevancy scorer 440, a frequencynormalizer 420, a distribution scorer 410 and an engagement scorer 415.In some embodiments, a distribution scorer 410 of the server maydetermine a distribution score for each content based on a number ofclicks from different sources via one or more encoded URL links to thecontent. The distribution scorer 410 may determine that a document has ahigher distribution score, or is more relevant to a search if clicks toaccess such a document arise from a broader set of source web sites. Thedistribution scorer 410 may determine the distribution of clicks for thesame document across a plurality of sites. The distribution scorer 410may track or evaluate the number of source sites that referred clicksfor a certain document.

In certain embodiments, the server may determine, via an engagementscorer 415, an engagement score for each content based on a number ofclicks received via one or more encoded URL links to the content. Eachof the number of clicks may be weighted based on when the click wasreceived. The engagement scorer 415 may determine the engagement scoreof a document based on the number of social clicks made to access thedocument. The engagement scorer 415 may rank content based on how manycorresponding clicks are received. In certain embodiments, theengagement scorer 415 applies a nonlinear function to click counts suchthat initial clicks are weighted more than later clicks.

In some embodiments, the server may determine, via a frequencynormalizer 420, a frequency normalization value for each content. Thefrequency normalizer 420 may extract keywords from the content,normalize the keyword counts and may store the keywords andcorresponding normalization values into a database (e.g., database 230).The frequency normalizer 420 may extract content from a document, suchas text-based content, suitable for text-based keyword searches. Thefrequency normalizer 420 may determine each term or word's frequency ofoccurrence in the extracted content. The frequency normalizer 420 maynormalize the counts of these terms or words. In some embodiments, thefrequency normalizer 420 may use td-idf normalization, such as Lucenescoring, to perform the keyword normalization. The frequency normalizer420 may provide a td-idf value for each document, to determine therelevancy score of the document.

In certain embodiments, the server may apply a time decay function tothe relevancy score based on the length of time a content has beenstored in a record after being identified from decoding the encoded URLlinks. In some embodiments, the time decay function is incorporated intothe calculation or formula for determining the social score or therelevancy score, for example, as described above in connection with FIG.4A. The time decay function may incorporate a log-linear time functionin certain embodiments. The time decay function may enable a documentwith links that are more recently clicked to have a higher social and/orrelevancy score.

The server may determine, via a search relevance scorer, the relevancyscore of a document by a combination of two or more of a social score, adistribution score, an engagement score, a frequency normalizationvalue, a time decay function, a source weighting component and a userweighting component. The search relevancy scorer may rank or indexdocuments based on any one or a combination of these scores or values.For example, the search relevancy scorer may use relevancy scores toreturn the highest scoring documents in a search, e.g., based on theclosest matching keywords.

In further details of (475), the server may communicate, responsive toreceiving a request to search content based on a keyword, a set ofsearch results based the keyword and the relevancy score. The server mayreceive one or more keywords or search terms from a user or applicationbased on a search. In some embodiments, the server The server mayprocess the one or more keywords or search terms into a search relevancequery 445, which may include a processed set of terms and/or priorityapplied to each of the processes terms. The search relevance query 445may comprise scores for keywords, indexes into specific groups ofdocuments and/or information about the user. In some embodiments, theserver may receive the search relevance query 445 from a search engineor an application. The server may apply or match one or a combination ofkeywords or terms against a listing, database or index of documents. Thelisting, database or index of documents may be ordered, ranked orindexed based on the documents' relevancy scores and/or document terms.The server may return a set of search results or documents based on therelevancy scores and/or closest matching terms of the documents. In someembodiments, the server may order the set of search results based onrelevancy score of the documents.

In certain embodiments, the server may match a plurality of documentsagainst one or a combination of keywords or terms without using therelevancy scores. The server may identify a subset of documents thatmost closely matches the one or a combination of keywords or terms. Theserver may rank this subset of documents based on the how closely thedocuments matches the one or a combination of keywords or terms. In someembodiments, the server combines this ranking with the relevancy scoreof these documents, to order the set of search results. The server may,for example, reorder the set of search results based on the relevancyscores or by applying a weighted preference based on the relevancyscores. Using embodiments of the above processes, the server maygenerate and/or order search results based on the relevance of thedocuments, e.g., as determined by user interaction, user feedback,and/or based on the popularity of particular content in connection withsocial media.

E. Systems and Methods for Identifying Trends in Phrases of Content

Referring now to FIGS. 5A and 5B, systems and methods for identifyingtrends in phrases in content is depicted. These systems and methods maydetermine trending or popular phrases from user interactions with webcontent containing, related to or associate with such phrases. Thesesystems and methods identify trending or temporally popular phrasesbased on aggregating multiple users' interactions with an aggregate ofcontent. Using click stream data and any list of phrases, ontology, ordictionary, systems and methods of the present solution can score webcontent based on users level of engagement with such content, and deducethe most popular phrases being viewed across a large set of content.These systems and methods may be applied in real-time or statically.These systems and methods may generate or provide a list of phrases ortopics that are trending upwards and/or downwards.

Referring now to FIG. 5A, an embodiment of a trending system 505 foridentifying trends in phrases related to, contained in, describing orotherwise associated with content for which users interact with or clickon is depicted. In brief overview, the system 505 includes a keywordextractor 515 that identifies phrases 520 from content identified via aclick stream 250′. The keyword extractor may also identify phrases 520from a predetermined set of web-sites 550. The keyword extractor mayoperate responsive a predetermined phrases list 522 by matching phrasesfound in content to this list. A trending engine 525 may receive thesephrases 520 as input and determine which phrases are trending up and/ordown or which phrases or topics are most popular. The trending enginemay include a velocity engine or component 530. The velocity engine maydetermine a number of clicks over a predetermined period of time for anyone or more phrases across any one or more sites. The trending enginemay generate an enumeration or list of phrases 540 that are trendingupwards and/or downwards. The list of phrases may be ranked according tothe change in trending, popularity or other criteria.

In further detail, the keyword extractor 515 may comprise anapplication, program, library, process, service, script, task or anytype and form of executable instructions for identifying, extracting, ordetermines keywords and/or phrases from, in, related to, describing orassociated with content, such as content the user clicked on an encodedURL link. The keyword extractor 515 may comprise any embodiments of thecontent extractor 315 described above in connection with FIG. 3A. Thekeyword extractor 515 may operate responsive to a phrases list 522. Insome embodiments, the keyword extractor may be configured with thephrases list. In some embodiments, the keyword extractor may read orprocess the phrases list from a file, data object or table of adatabase. The keyword extractor may be designed and constructed toidentify or detect keywords and/or phrases from the phrases list incontent from or of a digital resource, such as a web page identified bya URL.

The phrases list 522 may comprise any data and information identifying apredetermined set of phrases and/or keywords. The phrases list maycomprise a dictionary. The phrases list may comprise an ontology. Thephrases list may comprise an enumerated list of phrases and/or keywords.The phrases list may comprise an enumerated list of phrases and/orkeywords ranked in order of priority or otherwise having an identifiedpriority. The phrases list may comprise an enumerated list of phrasesand/or keywords ordered based on ranking or otherwise having anidentified ranking. The phrases list may comprise an enumerated list ofphrases and/or keywords with assigned weights or weighting. The phraseslist may identify a predetermined list of topics, interests or subjectmatter. The phrases list may identify a predetermined set of keywordsrelated to or making up a topics, interests or subject matter. Thephrases list may be generated from a third-party source, such as aweb-site or URL. The phrases list may be generated by the trendingengine based on a count of phrases and/or keywords identified in thepredetermined list of web-sites. The phrases list may be generated fromprevious versions of the phrases list. The phrases list may be generatedbased on learning or intelligence of the trending engine.

The keyword extractor may identify keywords responsive to one or moreclick streams 250′. In some embodiments, the keyword extractor operatesresponsive to receipt of a click stream or click action. In someembodiments, the keyword extractor operates in real-time as aclick-stream or portions thereof are received by the system 120. In someembodiments, the keyword extractor operates responsive to receipt of abatch of click streams or click actions. In some embodiments, thekeyword extractor operates responsive to a predetermined frequency,which may be configurable. In some embodiments, the keyword extractoroperates independently from the click stream and identifies keywordsfrom a predetermined set or list of web-sites 550. In some embodiments,the keyword extractor identifies keywords from a predetermined set orlist of web-sites 550 on a predetermined frequency. In some embodiments,the keyword extractor identifies keywords from a predetermined set orlist of web-sites 550 responsive to an event, such as a user request. Insome embodiments, the keyword extractor operates responsive to aclick-stream while identifying keywords from a predetermined set or listof web-sites 550.

The list or set of web-sites 550 may include an enumeration orconfiguration of a predetermined set or list of URLs, web-sites ordigital resources. The list or set of web-sites 550 may include a listof the most popular web-sites or URLs. The list or set of web-sites 550may include a list of the most visited web-sites or URLs. The list orset of web-sites 550 may include a list of the frequently visitedweb-sites or URLs. The list or set of web-sites 550 may include a listof the highest ranked web-sites or URLs. The list or set of web-sites550 may include a list of the most searched web-sites or URLs. The listor set of web-sites 550 may include a list of web-sites or URLs selectedby a user. The list or set of web-sites 550 may change based on changesin the ranking of any of these web-sites or URLs. The keyword extractormay be configured with the predetermined set or list of web-sites. Thekeyword extractor may be designed and constructed to read or process adata file, object or table of a database with the predetermined set orlist of web-sites. In some embodiments, the list of web-sites 550comprises a list of N (e.g. 1000) top sites on the internet by reach.This may be identified or pulled from Doubleclick's Top Sites[http://www.google.com/adplanner/static/top1000/] and may include all ofthe major social sites such as Facebook, Twitter, etc.

The keyword extractor may be designed and constructed to inspect, reador otherwise process any portion of content and match such portions tothe phrases list. The keyword extractor may strip images and/or othernon-textual elements from the content. The keyword extractor maysubtract common words from the textual portions of the content. Thekeyword extractor may be designed and constructed to inspect, read orotherwise process any text of content and match such text to the phraseslist. The keyword extractor may be designed and constructed to inspect,read or otherwise process any meta-data of content and match any stringsor text such meta-data to the phrases list. The keyword extractor may bedesigned and constructed to inspect, read or otherwise process any tags,scripts or mark-up language of content and match any strings or text ofsuch tags, scripts or mark-up language to the phrases list. The keywordextract may be designed and constructed to identify which phrasesdeviate from a norm relative to other phrases in the content.

The keyword extractor may be designed and constructed to generate,output or provide a set of phrases 520. The keyword extractor may bedesigned and constructed to interface to or communicate with thetrending engine 525. The keyword extractor may enumerate a set ofphrases and/or keywords based on a number of instances of the phraseand/or keyword. The keyword extractor may enumerate a set of phrasesand/or keywords based on a number of instances of the phrase and/orkeyword in the click stream. The keyword extractor may enumerate a setof phrases and/or keywords based on a number of clicks related to thephrase and/or keyword in the click stream. The keyword extractor mayenumerate a set of phrases and/or keywords based on a velocity of clicksrelated to the phrase and/or keyword in the click stream. The keywordextractor may enumerate a set of phrases and/or keywords based on anumber of instances of the phrase and/or keyword in the web-sites 550.The keyword extractor may enumerate a set of phrases and/or keywordsbased on a number of instances of the phrase and/or keyword in both theclick stream and in the web-sites. The keyword extractor may enumerate aset of phrases and/or keywords based on an order or ranking from thephrases list 522. The keyword extractor may enumerate a set of phrasesand/or keywords based on a corresponding weighting from the phrases list522. The keyword extractor may enumerate a set of phrases and/orkeywords based on temporal information. The keyword extractor mayenumerate a set of phrases and/or keywords on a real-time basis as theyare generated. The keyword extractor may enumerate a set of phrasesand/or keywords on a predetermined basis, such as on a predeterminedschedule or at a predetermined frequency.

The keyword extractor may filter the list of phrases based on ranking,priority or weighting, such as may be specified by the phrases list. Thekeyword extractor may filter the list of phrases based on apredetermined threshold, such as a number of instances of identificationof the phrase across content. The keyword extractor may filter the listof phrases based on temporal information and thresholds, such as anumber of instances of identification of the phrase across content overa predetermined time period. The keyword extractor may filter the listof phrases based on geography. The keyword extractor may filter the listof phrases based on user profiles. The keyword extractor may filter thelist of phrases based on source, such as via click streams or via thepredetermined web-sites.

The trending engine 525 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions. The trending engine may comprise functions, operations orlogic to identify trends in phrases and/or keywords across digitalresources interacted with by users, such as via clicking on encodedlinks to content related to, described by or containing the phrasesand/or keywords. The trending engine may be designed and constructed toprocess the phrases 520 from the keyword extractor and to determinewhich of those phrases are trending up and/or down based on userinteractions, such as clicking, with digital resources associated with,connected to or comprising those phrases. The trending engine may bedesigned and constructed to identify which phrases deviate from a norm.The trending engine may be designed and constructed to process thephrases 520 from the keyword extractor and to determine which of thosephrases are most popular. The trending engine may be designed andconstructed to process the phrases 520 from the keyword extractor and todetermine which phrases a user or set of users interact with the mostand/or the least. The trending engine may be designed and constructed toprocess the phrases 520 from the keyword extractor and to determinewhich of those phrases are from content of an encoded URL that usershave clicked on the most and/or the least. The trending engine may bedesigned and constructed to process the phrases 520 from the keywordextractor and to determine which of those phrases are from content of anencoded URL that users have shared the most and/or the least. Thetrending engine may be designed and constructed to process the phrases520 from the keyword extractor and to determine which of those phrasesare from content of a URL or web page that has been visited or servedthe most and/or the least.

The trending engine may comprise any embodiments of the relevance system405 described in connection with FIGS. 4A and 4B. For example, thetrending engine may comprise any embodiments of the distribution scorer410, engagement scorer 415 and/or time decay function 435 described inconnection with FIGS. 4A and 4B. Any embodiments of distribution scorer410, engagement scorer 415 and/or time decay function 435 may be appliedto content associated with or comprising the phrases and based on theresults the trending engine determines the trending phrases. Forexample, in some embodiments, the trending engine determines thosephrases from or associated with content with the highest raw socialscore. In some embodiments, the trending engine determines those phrasesfrom or associated with content with the raw social score greater than athreshold. In some embodiments, the trending engine determines thosephrases from or associated with content with the mapped social scoregreater than a threshold. In some embodiments, the trending enginedetermines those phrases from or associated with content with the totalsocial score greater than a threshold. In some embodiments, the trendingengine determines those phrases from or associated with content with thehighest number of social referrers or a total number of social referrersgreater than a threshold. In some embodiments, the trending enginedetermines those phrases from or associated with content with thehighest number of mapped social referrers or a total number of mappedsocial referrers greater than a threshold. Any of the scores from thetrending engine, such as via the distribution and/or social scorer, maybe decayed via the time decay function and the resulting score ofcontent used to identify the trending phrases accordingly.

The trending engine may be designed and constructed to determine avelocity of interaction with content associated with, related to orcontaining the phrases. The trending engine may determine such velocityvia a velocity engine or component. A velocity engine 530 may comprisean application, program, library, process, service, script, task or anytype and form of executable instructions. The trending engine mayinclude the velocity engine. In some embodiments, the velocity engine isseparate from the trending engine and the trending engine maycommunicate with or interface to the velocity engine. The velocityengine may be designed and constructed to determine any change in therate of interaction over time with content associated with, related toor contains with one or more phrases. The velocity engine may bedesigned and constructed to determine and/or track a number of clicks onan encoded URL over a predetermined time period in which the contentfrom or of the encoded URL is associated with, related to or containsthe phrase. The velocity engine may be designed and constructed todetermine and/or track a number of clicks on a plurality of encoded URLsover a predetermined time period in which content from the plurality ofencoded URLs is associated with, related to or contains the phrase. Thevelocity engine may be designed and constructed to determine and/ortrack the velocity of upward or downward trends of a phrase. Thevelocity engine may be designed and constructed to determine and/ortrack the velocity of popularity of a phrase. The velocity engine may bedesigned and constructed to determine and/or track the velocity ofserving or visiting content comprising a phrase.

The trending engine may generate, output, communicate or otherwiseprovide a list or set of one or more trending phrases 540. The output540 may be an enumerated list or ordered list. The output may be areport. The output may be a file. The output may be data stored in adatabase. The output may be a web page comprising the trending phrases.The output may be any digital resource comprising or identifying thetrending phrases. The trending engine may output the set of trendingphrases via an API call, event or function to an application, program orsystem. For example, the trending engine may output the set of trendingphrases via XML. The trending engine may output the set of trendingphrases via a web service call or response to a web service call. Thetrending engine may output the set of trending phrases via raising anevent or calling a function.

The output may be an encoded URL identifying a digital resourcecomprising or identifying the trending phrases. In some embodiments, thetrending phrases or output 540 comprises a list of phrases that aretrending upwards. In some embodiments, the trending phrases or output540 comprises a list of phrases that are trending upwards above, belowor within a predetermined threshold. In some embodiments, the trendingphrases or output 540 comprises a list of phrases that are trendingdownwards. In some embodiments, the trending phrases or output 540comprises a list of phrases that are trending downwards above, below orwithin a predetermined threshold. The trending phrases or output 540 maybe in ascending or descending order.

In the output 540, the trending engine may identify for each or some ofthe phrases in the phrases list a ranking or placement in the ranking.For each of the phrases from the phrases 520 and/or phrases list, thetrending engine may determine a change in the ranking or the placementof the phrase from a previous instance of producing output 540 by thetrending engine. In the output, for each of the phrases from the phrases520 and/or phrases list, the trending engine may determine a change inthe ranking or the placement of the phrase during a predetermined timeperiod. For each of the phrases from the phrases 520 and/or phraseslist, the trending engine may determine a percentage or degree change inthe ranking or the placement of the phrase from a previous instance ofproducing output 540 by the trending engine. For each of the phrasesfrom the phrases 520 and/or phrases list, the trending engine maydetermine a percentage or degree change in the ranking or the placementof the phrase over a predetermined time period.

Referring now to FIG. 5B, an embodiment of a method for identifyingtrends in phrases based on an aggregate of users interactions with anaggregate of content is depicted. In brief overview, the method, at step555, a server, such as via linking system 120, receives a click stream.At step 560, the server identifies phrases from content of decoded linksfrom the click stream. The server may also identify phrases from contentof a predetermined list of web-sites. At step 565, the trending enginedetermined trending phrases, such as based on click velocity. At step570, the trending engine generates a set of trending phases, such asresponsive to determined click velocity.

At step 555, a server or system, such as linking system 120, receivesone or more click streams. The server may receive user actions via clickstreams 250′. The server may a click stream via or comprising a userinteracting, user or accessing a digital resource. The server mayreceive a click stream from the digital resource. The server may receivea click stream via a user requesting to encode an URL. The server mayreceive a click stream via a user clicking on an encoded URL. The servermay receive a click stream via a request to decode an encoded URL. Theserver may receive a clicks stream via a client linking system API orapplication 225. The server may receive a click stream via a log file oractivity log from an application, system, device or server. The servermay decode any encoded URLs of the received click streams to identifyassociated content or content of the URL. The server may decode anyencoded URLs upon receipt of the click stream.

At step 560, the server or system, such as via the keyword extractor,identifies phrases from, associated with, describing or related tocontent that users are interacting with or clicking on. The keywordextractor may identify phrases corresponding to a list of phrases 522from content identified via decoding of encoded URLs. The keywordextractor may operate responsive to receipt of a user action or a clickstream. The keyword extractor may identify phrases corresponding to alist of phrases 522 from content of a predetermined set or list ofweb-sites or URLs. The keyword extractor may identify phrasescorresponding to a list of phrases 522 from content of a predeterminedset or list of digital resources. The keyword extractor may operateresponsive to a predetermined schedule for extracting or identifyingkeywords from these web-sites, URLs or digital resources. The keywordextractor may operate responsive to a change in the list of web-sites,URLs or digital resources. The keyword extractor may filter the phraselist according to ranking, priority, weighting, geography or temporalinformation. The keyword extractor may filter the list of phrases basedon a threshold.

At step 565, the trending engine determines trends in the phrases, suchas the phrases provided or generated by the keyword extractor. Thetrending engine may determine trends responsive to receipt of phrasesfrom the keyword extractor. The trending engine may determine trendsresponsive to a predetermined schedule. The trending engine maydetermine trends on demand, such as responsive to a user request. Thetrending engine may determine trends responsive to any combination ofthe distribution scorer, engagement scorer and time decay functions. Thetrending engine may determine trending phrases from content with thehighest or higher scores from the distribution scorer, engagement scorerand/or time decay functions. The trending engine may determine thenumber of instances of user action with a digital resource associatedwith or comprising one or more phrases. The trending engine maydetermine the number of instances over a predetermined time period ofuser action with a digital resource associated with or comprising one ormore phrases. The trending engine, such as via the velocity engine, maydetermine a velocity of interaction by users with digital resources,such as content, associated with, related to or containing the phrases.The trending engine, such as via the velocity engine, may determine avelocity of interaction by users with content associated with, relatedto or containing the phrases. The trending engine may determine avelocity of user actions on with content associated with, related to orcontaining the phrases. The trending engine may determine a velocity ofclick actions to content associated with, related to or containing thephrases.

At step 570, the trending engine produces or generates a set of trendingphrases responsive to determining the trends in the phrases. Thetrending engine may output the set of trending phrases responsive to thedetermination(s) of step 565. The trending engine may output the set oftrending phrases responsive to a predetermined schedule. The trendingengine may output the set of trending phrases on demand, such asresponsive to a user request. The trending engine may output the set oftrending phrases via an API call, event or function to an application,program or system. The trending engine may output a ranking of trendingphrases, such as top N most upward trending phrases or top N mostdownward trending phrase. The trending engine may output a ranking oftrending phrases, such as top N most popular phrases or top N leastpopular phrases. The trending engine may output an ordered list oftrending phrases in increasing or decreasing velocity. The trendingengine may output an ordered list of trending phrases with greatestchange in velocity. The trending engine may output an ordered list oftrending phrases with slowing or least amount of change in velocity.

Although the systems and methods may be generally described herein inreference to phrases, the systems and methods may be designed andconstructed to determine a trending topic corresponding to a set orgroup of phrases. For example, the trending engine may be designed andconstructed to organize or arrange a group of phrases into a topic. Thetrending engine may be designed and constructed to associate or identifythat a group of phrases correspond to or describe a topic. In someembodiments, the phrases list may be constructed or organized toassociate phrases with topics and the keyword extractor and trendingengine operate responsive to this embodiment of the phrases list. Any ofthe systems and methods described herein may operate or be responsive toa group of phrases and produce a set of trending topics in accordancewith the embodiments described herein.

F. Systems and Methods for Identifying Trends and Relevance of Phrasesfor a User

Referring now to FIGS. 6A and 6B, systems and methods for identifyingphrases that are relevant to specific user from the phrases that aretrending among an aggregate of users interactions with an aggregate ofcontent relevance is depicted. The embodiments of the systems andmethods described in connection with FIGS. 3A and 3B for relevancescoring may be combined with embodiments of the systems and methodsdescribed in connection with FIGS. 5A and 5B for trending to providesystems and methods for identifying trends in phrases across multipleusers and the relevance of such phrases for a particular user. Bycombining trending phrases with relevance scoring, the systems andmethods of embodiments of the present solution can determine whichcontent or phrases is most relevant to a specific user based on trendingphrases from an aggregate set of users.

Referring now to FIG. 6A, a system for identifying those phrasestrending for an aggregate set of users which is relevant to a specificuser is depicted. In brief overview, the system includes a trending andrelevance engine 605 which may include a trending system 505 and arelevance system 300. The system may receive identification of a uservia user id 345. The system identifies via the trending system thosephrases that are trending, such as across an aggregate set of usersinteractions (e.g., clicks) with an aggregate of content. Furtherresponsive to the user id, the system identifies those trending phrasesfrom the aggregate set of user interactions that have the most relevantscore to the user via the relevance system. The system outputs a list ofphrases relevant and trending to the user 610. A content selector 620may use this list to identify the most relevant content to provide orserver the user.

The system may include any embodiments of the trending system 505described in connection with FIGS. 5A and 5B. The system may include anyembodiments of the relevance system 300 described in connection withFIGS. 3A and 3B. The system may include any embodiments of the relevancesearch system 405 described in connection with FIGS. 4A and 4B. Thetrending system may be designed and constructed to interface to,communicate to or integrate with the relevance system. The trendingsystem may provide a list of phrases 540 to the relevance system. Therelevance system may be designed and constructed to interface to,communicate to or integrate with the trending system. The relevancesystem may provide a list of digital resources and relevance scores 330to the trending system. The trending system and relevance system may bedesigned and constructed to work in cooperation or in conjunction witheach other. In some embodiments, the trending system and relevancesystem are combined or constructed into a single application, component,module or system. Any of the above embodiments may be generally referredto as the trending and relevance engine 605 or the relevance andtrending engine 605. The system may apply any of the scoring andweighting functions of any embodiments of the relevance system 300/405described herein in combination with or otherwise to trending phrases.

The trending and relevance engine may comprise any functionality,operations and/or logic to identify phrases that are trending for theuser identified by the user id. The trending and relevance engine maycomprise any functionality, operations and/or logic to identify digitalresources that are most relevant to the user. The trending and relevanceengine may comprise any functionality, operations and/or logic toidentify phrases that are trending upwards and/or downwards in digitalresources interacted with by an aggregate of users and that are most ormore relevant to the user. The trending and relevance engine maycomprise any functionality, operations and/or logic to identify phrasesthat are trending upwards and/or downwards in digital resourcesinteracted with by an aggregate of users and that are least or lessrelevant to the user. The trending and relevance engine may identifyphrases that are trending in digital resources that the user isinteracting with. The trending and relevance engine may identify phrasesthat are trending (upwards or downwards) in digital resources interactedwith by an aggregate of users and having a relevance score for the usergreater than a predetermined threshold. The trending and relevanceengine may identify phrases that are trending (upwards or downwards) indigital resources interacted with by an aggregate of users and having arelevance score for the user less than a predetermined threshold. Thetrending and relevance engine may identify phrases that are trending(upwards or downwards) in digital resources interacted with by anaggregate of users and having a relevance score for the user andvelocity greater than a predetermined threshold.

The trending and relevance engine may generate or calculate a relevancescore for content that the user interacted with and for which includesone or more phrases corresponding to the list of phrases 522. In someembodiments, while the trending and relevance engine determines trendingphrases in content for which users have interacted with, the trendingand relevance engine may also determine a relevance score of howrelevant that content is to the user. In some embodiments, the trendingportion of the trending and relevance engine identifies the trendingphrases content corresponding to the trending phrases. The relevanceportion of the trending and relevance engine may provide a relevancescore for the trending phrase or the content corresponding to each ofthe trending phrases. In some embodiments, the relevance portion of thetrending and relevance engine identifies from the trending phrase orcorresponding content, those trending phrases or content most relevantto the user. In some embodiments, the trending and relevance engineperforms relevance scoring on each content of a plurality of contentcorresponding to a phrase and takes an average, weighted average orother function of these scores to provide a relevance score for allcontent associated with a phrase.

The trending and relevance engine may identify trending or temporallypopular phrases based on aggregating multiple users' interactions withan aggregate of content and comparing such phrases to a list of phrasesof a particular user via the user's click history and/or user profile.The trending and relevance engine may match phrases between theaggregate user's trending phrases and the phrases from the user'shistory or profile to provide a set of trending phrases for the user. Insome embodiments, the match of phrases from the trending phrases of theaggregate users to the phrases of the user may be referred to astrending phrases of or for the user. The trending and relevance enginemay perform a relevance score for such matching or trending phrases ofthe user, or for any content associated with such phrases.

As a result of operation, the trending and relevance engine identifiesphrases that are trending (upwards and/or downwards) in content that anaggregate of users is interacting with/clicking on and which is most ormore relevant (and/or less or least relevant) to the user based on therelevance score. The relevance and trending engine may generate, output,communicate or otherwise provide a list or set of one or more userspecific relevant trending phrases 610. The output 610 may be anenumerated list or ordered list. The output may be a report. The outputmay be a file. The output may be data stored in a database. The outputmay be a web page comprising the user relevant trending phrases. Theoutput may be any digital resource comprising or identifying the userrelevant trending phrases. The trending engine may output the set ofrelevant trending phrases via an API call, event or function to anapplication, program or system. For example, the trending and relevanceengine may output the set of user relevant trending phrases via XML. Thetrending engine may output the set of relevant trending phrases via aweb service call or response to a web service call. The trending andrelevance engine may output the set of user relevant rending phrases viaraising an event or calling a function.

The output 610 may be an encoded URL identifying a digital resourcecomprising or identifying the user relevant trending phrases. In someembodiments, the user relevant trending phrases or output 610 comprisesa list of phrases that are trending upwards in content most relevant tothe user. In some embodiments, the user relevant trending phrases oroutput 610 comprises a list of phrases that are trending upwards incontent least relevant or becoming less relevant to the user. In someembodiments, the user relevant trending phrases or output 610 comprisesa list of phrases that are trending upwards above, below or within apredetermined threshold are most relevant and/or least relevant to theuser. In some embodiments, the user relevant trending phrases or output610 comprises a list of phrases that are trending downwards and are mostand/or least relevant to the user. In some embodiments, the userrelevant trending phrases or output 610 comprises a list of phrases thatare trending downwards in content becoming less relevant or leastrelevant to the user. In some embodiments, the user relevant trendingphrases or output 610 comprises a list of phrases that are trendingdownwards above, below or within a predetermined threshold in contentmost and/or least relevant to the user.

In the output 610, the relevance and trending engine may identify foreach of the phrases for the user a ranking or placement in the ranking.The relevance and trending engine may determine a change in the rankingor the placement of the phrase from a previous instance of producingoutput 610 by the relevance and trending engine. In the output, therelevance and trending engine may identify a change in the ranking orthe placement of the phrase during a predetermined time period. Therelevance and trending engine may determine a percentage or degreechange in the ranking or the placement of the phrase from a previousinstance of producing output 540 by the relevance and trending engine.In the output, the relevance and trending engine may identify apercentage or degree change in the ranking or the placement of thephrase over a predetermined time period. In the output, the relevanceand trending engine may identify a relevance score for each of thetrending phrases. In the output, the relevance and trending engine mayidentify a relevance score and a trending indicator for each of thetrending phrases for the user.

A content selector 620 may select or identify content to serve a userbased on the output 610 from the trending and relevance engine 605. Thecontent selector 620 may comprise an application, program, library,process, service, script, task or any type and form of executableinstructions. The content selector may operate responsive to trendingand relevance engine. The content selector may select content to servera user based on or using any information provided in any embodiments ofthe list of user relevant trending phrases 610. In some embodiments, thecontent selector 620 operates or executes on a system, server, orapplication in communication over a network to the trending andrelevance engine. In some embodiments, the content selector may beembedded or included in a web page or other content served by thesystem, server or application. A content selector may identify from aplurality of content from one or more web-sites the user is visiting,the content that has highest trending phrases and is most relevant touser. Responsive to identifying such content, the system, server, orapplication may serve or provide the content to the user.

Referring now to FIG. 6A, an embodiment of a method for identifyingtrending or temporally popular phrases based on aggregating multipleusers' interactions with an aggregate of content and that are relevantto a specific user is depicted. In brief overview, at step 655, thesystem receives identification of the user. At step 660, the systemdetermines trending phrases for the user. At step 665, the systemdetermines relevance score of content associated with or comprising thetrending phrases. At step 670, the system identifies user relevanttrending phrases. At step 675, content to serve the user is selectedbased on the user relevant trending phrases.

In further details, at step 655, the trending and relevance enginereceives identification of a user via any type and form of user id. Thetrending and relevance engine may receive a request to provide a list ofuser relevant trending phrases for a user identified by the useridentifier. In some embodiments, the trending and relevance enginereceives the user id via a cookie. The trending and relevance engine mayreceive a user identifier or a request from any application, system orserver. In some embodiments, a third party web site serving content maytransmit the request and a user identifier to the trending and relevanceengine. In some embodiments, an ad server serving advertisement ormatching content to impression opportunities may transmit the requestand/or a user identifier to the trending and relevance engine scorer. Insome embodiments, a client application may transmit the request and/or auser identifier to the trending and relevance engine.

At step 660, the trending and relevance engine determines phrases thatare trending based on the aggregate of multiple users' interactions withan aggregate of content and which are relevant, such based on arelevance score, to the user identified by the user id. The trending andrelevance engine may determine trending phrases based on any embodimentsof the systems and methods described in connection with FIGS. 5A and 5B.For example, the trending and relevance engine may determine whichphrases are trending for the aggregate of users and compare thosephrases with a click history or user profile of the user specified bythe user id. The trending and relevance engine may perform or provide arelevance score for those matching phrases.

In another example, the trending and relevance engine may determinetrending phrases for the user based on the number of instances of useraction by the user with a digital resource associated with, related toor containing one or more phrases. The trending and relevance engine maydetermine trending phrases by the user based on the number of instancesover a predetermined time period of user action by the user with adigital resource associated with, related to or containing one or morephrases. The trending and relevance engine, such as via the velocityengine, may determine trending phrases for the user based on a velocityof interaction by the user with digital resources, such as content,associated with, related to or containing the phrases. The trending andrelevance engine, such as via the velocity engine, may determinetrending phrases for the user based on a velocity of interaction by theuser with content associated with, related to or containing the phrases.The trending and relevance engine may determine trending phrases for theuser based on a velocity of user actions by the user with contentassociated with, related to or containing the phrases. The trending andrelevance engine may determine trending phrases for the user based on avelocity of click actions by the user to content containing, related toor otherwise associated with the phrases.

At step 665, the trending and relevance engine determines for the useridentified by the user id the relevance of the phrases from the trendingphrases. In some embodiments, step 665 is performed in conjunction with,during or as part of step 660. For the content containing, related to orassociated with the trending phrases, the trending and relevance enginedetermines a relevance score for the user for such phrases or content.In some embodiments, the trending and relevance engine determines arelevance score for phrases or content associated with phrases trendingupwards for the user. In some embodiments, the trending and relevanceengine determines a relevance score for phrases or content trendingdownwards for the user. In some embodiments, the trending and relevanceengine determines a relevance score for phrases or content associatedwith phrases trending upwards and/or downwards for the user within apredetermined threshold. In some embodiments, the trending and relevanceengine determines a relevance score for phrases or content associatedwith phrases trending upwards and/or downwards for the user within apredetermined time period. In some embodiments, the trending andrelevance engine determines a relevance score for phrases or contentassociated with phrases trending upwards and/or downwards for the userwith or within a predetermined velocity. In some embodiments, thetrending and relevance engine determines a relevance score for phrasesor content associated with a top number of trending phrases.

At step 670, the trending and relevance engine generates or provides alist of user relevant trending phrases 610. The trending and relevanceengine may generate or provide a list of user relevant trending phrases610 responsive to steps 655, 660 and/or 665. The trending and relevanceengine may enumerate the list of user relevant trending phrases 610 inascending or descending order. The trending and relevance engine mayenumerate the list of user relevant trending phrases 610 based onrelevance. The trending and relevance engine may enumerate the list ofuser relevant trending phrases 610 based on trend velocity. The trendingand relevance engine may enumerate the list of user relevant trendingphrases 610 based on relevance and trend velocity. The trending andrelevance engine may enumerate the list of user relevant trendingphrases 610 based on a function of relevance and trend velocity. Thetrending and relevance engine communicates or provides a list of userrelevant trending phrases 610 to a requestor, such as a user,application, server or system. The trending and relevance enginecommunicates or provides a list of user relevant trending phrases 610 tothe content selector.

At 675, a content selector may select content to serve the userresponsive to the trending and relevance engine. The content selectormay select content from a plurality of possible content to serve theuser based on the user relevant trending phrases. The content selectormay select content from a plurality of possible content to serve theuser based on the highest trending phrase in the user relevant trendingphrases. The content selector may select content from a plurality ofpossible content to serve the user based on the most relevant phrase inthe user relevant trending phrases. The content selector may selectcontent from a plurality of possible content to serve the user based onthe most relevant and highest trending phrase in the user relevanttrending phrases. In some embodiments, the content selector provides adigital resource id and user id to the trending and relevance engine todetermine what content to select and serve to the user.

G. Systems and Methods for Tracking Influence of a User on ContentShared Via Encoded Uniform Resource Locator (URL) Link

Referring now to FIGS. 7A and 7B, systems and methods for trackinginfluence of a user on content shared via encoded URLs is depicted.Influence of a user, generally referred to as influence, may identifywhat level of engagement by other users does a particular user drive tocontent when the user shares content. For example, a high influencer maybe a user who drives a high level of engagement with content when theuser shares content. A low influencer may be a user who does not drivesa high level of engagement, or otherwise drivers a low level ofengagement with content when the user shares content. To measure or rateinfluence, embodiments of the systems and methods of the presentsolution may correlate who encoded a link, how important or how muchtraffic does the encoded link generate, who clicked on the encoded linkand the topics or phrases associated with the links. From thiscorrelation, these systems and methods may determine a ranking or ratingof the influence of a user which may be stored in the user's profile.The influence rating of the user may be used by a relevance system toprovide user weighting to relevance and trending scoring systems, suchas any embodiments of such systems herein.

Referring now to FIG. 7A, a system for tracking influence of a user isdepicted. In brief overview, the system may include a keyword extractor515, user tracker 215 and click tracker 220 as previously describedherein. The system may include an influence tracker 720 that correlatesinformation from the keyword extractor 515, user tracker 215 and clicktracker 220 to determine an influence rating 712A-712N (generallyreferred to as influence rating 712) for each of a plurality of users,which may be stored in the user's profile 710A-710N (generally referredto as user profile 710). Based on the influence rating of a user, arelevance system 605 may provide user weighting 430 to a relevance scoreto up weight or down weight the score to result in a user influencerelevance score 725. A user's influence rating 712 may be queried orobtained by request using a user identifier 345.

The trending and relevance system 605 may comprise any embodiments ofthe trending and relevance system 605 described herein. In someembodiments, the trending and relevance system 605 comprises thetrending system 405. In some embodiments, the trending and relevancesystem 605 comprises the trending system 505. In some embodiments, thetrending and relevance system 605 comprises the relevance system 300. Insome embodiments, the trending and relevance system 605 comprises therelevance system 300. In some embodiments, the trending and relevancesystem 605 comprises the relevance system 405. Any of these embodimentsmay be referred to as a relevance based system.

Each of the keyword extractor 515, user tracker 215 and click tracker220 may comprise any embodiments of keyword extractor 515, user tracker215 and click tracker 220 described herein. The keyword extractor, usertracker and click tracker may be designed and constructed to interfaceto, communicate with or integrate to the influence tracker. Theinfluence tracker may be designed and constructed to interface to,communicate with or integrate to keyword extractor, user tracker and/orclick tracker. The keyword extractor, user tracker and click tracker maybe designed and constructed to work in cooperation or in conjunctionwith the influence tracker. In some embodiments, the keyword extractor,user tracker, click tracker and influence tracker 720 are combined orconstructed into a single application, component, module or system. Anyof the above embodiments may be generally referred to as an influencesystem or the influence tracker 720.

In further details, the influence tracker 720 may comprise anapplication, program, library, process, service, script, task or anytype and form of executable instructions. The influence tracker maycomprise functions, operations and logic to determine the influence of auser based on information tracked by the linking system relative to,associated with or in connection with a specific user. In someembodiments, the influence tracker may operate responsive to the linkencoder and/or link decoder. In some embodiments, the influence trackermay operate responsive to the user tracker. In some embodiments, theinfluence tracker may operate responsive to the click tracker and/orclick analyzer 235.

The influence tracker may identify, track and correlate information onwhat links the user encoded, the traffic generated by the user's encodedlinks, who clicked on the user's encoded links and topics or phrasesassociated with the content from the user's encoded links. In someembodiments, the influence tracker identifies and tracks the URLsencoded by a user. In some embodiments, the influence tracker identifiesand tracks the number of URLs encoded by a user, such as via the linkencoder and user tracker. In some embodiments, the influence trackeridentifies and tracks the content from the URL encoded by the user. Insome embodiments, the influence tracker identifies and tracks keywordsand/or phrases of the content from the URL encoded by the user. In someembodiments, the influence tracker identifies and tracks topics orsubject matter of the content from the URL encoded by the user. In someembodiments, the influence tracker identifies and tracks trendingphrases of the content from the URL encoded by the user. In someembodiments, the influence tracker identifies and tracks the number ofclicks and/or number of other users who clicked on the encoded URLencoded by the user. In some embodiments, the influence trackeridentifies and tracks the sources or sites from which of other usersclicked on the encoded URL encoded by the user.

The influence tracker may store any of the identified and trackedinformation associated with the user to a user profile 710. The userprofile may comprise a data structure, data object, file or one or moretables, such as data, objects or tables stored in a database. The userprofile may store an aggregation of the information identified andtracked by the influence tracker. The user profile may store anystatistics or metrics of the information identified and tracked by theinfluence tracker. The user profile may store a history of any theinformation identified and tracked by the influence tracker. The userprofile may store influence rating of the user. The user profile maystore a history of influence ratings of the user, such as changes andupdated to the influence rating of the user.

The influence tracker may process, analyze and correlate any of theinformation tracked by the system of the user, such as any informationtracked via the above described embodiments or stored in the userprofile, to determine, generate or otherwise provide an influence ratingor score 712 for the user. The influence rating or score of a useridentifies a level of engagement by other users that a particular userdrives or causes when sharing content (e.g. how much influence as userhas for others to interact or engage with content when the user sharescontent). An influential user is someone who drives or causes a higheror high level of engagement or interaction with content when the usershares content such as via forwarding or distributing encoded links. Theinfluence rating may identify a number of clicks from other users that aparticular user drives when sharing content. In some embodiments, theinfluence tracker generates the influence rating 712 based on applying afunction or algorithm to any combination of number of encoded URLsencoded by the user, how much traffic (e.g., number of clicks) generatedby encoded URLs encoded by the user, who clicked on the encoded URLsencoded by the user and the topics associated with the encoded URLsencoded by the user. In some embodiments, the influence tracker ingenerating or computing the influence rating may weight any of thecomponents or factors making up the influence rating in any manner. Insome embodiments, the influence tracker uses a time decay function 435to modify, change or affect the influence rating based on temporalinformation related to user and any of the components or factors makingup the influence rating.

For example, in some embodiments, the influence tracker may compute theinfluence rating as function of a number of URLs encoded by the user andthe number of clicks on the encoded URLs encoded by the user. In anotherexample, the influence tracker may compute the influence rating asfunction of a number of URLs encoded by the user and/or the number ofclicks on the encoded URLs encoded by the user and the influence ratingof users who clicked on the encoded URL of the user. In another example,the influence tracker may compute the influence rating as function of anumber of URLs encoded by the user and/or the number of clicks on theencoded URLs encoded by the user and the popularity or ranking of theweb-sites from which users clicked on the encoded URL of the user. Inanother example, the influence tracker may compute the influence ratingas function of a number of URLs encoded by the user and/or the number ofclicks on the encoded URLs encoded by the user and the popularity orranking of the phrases, keywords or topics of the content from orassociated with the encoded URL of the user. In another example, theinfluence tracker may compute the influence rating as function of anumber of URLs encoded by the user and/or the number of clicks on theencoded URLs encoded by the user and the trending of phrases, keywordsor topics of the content from or associated with the encoded URL of theuser. In another example, the influence tracker may compute theinfluence rating as a function of a relevance score for content ofencoded URLs of the user. In another example, the influence tracker maycompute the influence rating as a function of an engagement score and/ordistribution score for content of encoded URLs of the user. In anotherexample, the influence tracker may compute the influence rating as afunction of geography of the user and/or the users clicking on theuser's encoded URL.

In another example, the influence tracker may compute the influencerating as a function of any combination of a relevance score, engagementscore, distribution score, social score, geography, search relevancescore, a number of URLs encoded by the user and/or the number of clickson the encoded URLs encoded by the user, popularity or ranking ofweb-sites from which users clicked the encoded URL of the user,popularity or ranking of the phrases, keywords or topics of the contentfrom or associated with the encoded URL of the user and/or the trendingof phrases, keywords or topics of the content from or associated withthe encoded URL of the user

The influence rating 712 may comprise a value that provides anindication of or otherwise identifies how influential a user, such asthe influence of a user when sharing encoded URLs. The influence ratingmay be generated for or on an absolute or relative scale. The influencerating may be generated for or normalized to a predetermined influencerating range, such as for example −100 to 100, 0 to 100 or X to Y. Theinfluence tracker may store the influence rating to the user's profile.The influence tracker may update the influence rating in the user'sprofile.

As click streams are received over time by the system 120, the influencetracker may regenerate, re-compute or re-determine the influence ratingof a user. In some embodiments, the influence tracker determines theinfluence rating dynamically in real-time. As a click stream is receivedthat impacts or affects the user's influence, the influence tracker mayupdate the user's profile with tracked information and regenerate theuser's influence rating. In some embodiments, the influence trackerdetermines the influence rating on a predetermined basis, such as once aday at a certain time. In some embodiments, the influence trackerdetermines the influence rating on an adhoc or on-demand basis, such asresponsive to a request for the user's influence rating. In some theinfluence tracker determines the influence rating on an event basis,such as when the user encodes a URL or the system receives a user actionfrom a click of the encoded URL encoded by the particular user.

The influence rating 712 and/or user profile may provide for or impactthe user weighting 430 applied during any trending and/or relevancescoring system 605 described herein. In some embodiments, the influencerating is the user weighting. In some embodiments, the user weighting isa function of the influence rating. In some embodiments, the system 605may convert the influence rating using any type and form of scaling orconversion factor to the user weighting 430. In some embodiments, thesystem 605 queries the influence tracker for the influence rating of auser identified by the user id 345. In some embodiments, the system 120uses the influence rating 712 to up weight or down weight any relevancescore and/or trending indicator. In some embodiments, the system 120uses information in user profile(s) to up weight or down weight anyrelevance score and/or trending indicator.

Based on applying the influence rating and/or user weighting and/or theuser profile, the system 605 provides a user influenced relevance score725. In some embodiments, the relevance scores 325 described in FIG. 3Amay use the influence rating 712 and/or user weighting 430 to determineor generate a relevance score 330 that is a user influenced relevancescore 725. In some embodiments, the user influenced relevance score maycomprises the relevance score(s) for content in the relevance searchresults 450 as described in FIG. 4A with the user weighting 430 based onthe influence rating 712 of a specified user. In some embodiments, thetrending/ranked phrases 540 described in FIG. 5A may use the influencerating 712 and/or user weighting 430 to determine or generate a userinfluenced relevance score 725 for such trending/ranked phrases. In someembodiments, the relevant trending phrases 610 described in FIG. 6A mayuse the influence rating 712 and/or user weighting 430 to determine orgenerate a user influenced relevance score 725 for such relevanttrending phrases 610.

Referring now to FIG. 7B, a method for tracking influence of a user isdepicted. In brief overview of the method at step 755, the systemreceives identification of the user for each encoded URL. At step 760,the system identifies keywords, phrases and topics from contentassociated with each encoded URL. At step 765, the system determines anumber actions and other metrics via user uses the decoded each encodedURL of the user. At step 770, the system generates an influence ratingand/or user weighting. At step 775, the system may up weight or downweight a relevance score based on a profile of the user, such as theuser's influence rating.

In further details, at step 755, the system identifies the user encodingthe URL. In some embodiments, the system identifies the user via a userid 345. In some embodiments, the system identifies the user via anaccount or login of the linking system 120. In some embodiments, thesystem identifies the user via a cookie 255. The system such as via thelink encoder and user tracker may track each instance of the userencoding a link and store that information to the database 230 and/or tothe user's profile 710. The system may track when the user encoded thelink, what link was encoded and identification of the encoded link andstore this information to the user's user profile.

At step 760, the system may identify keywords, phrases and/or topicsfrom the content associated with, identified by or included in the URLor link encoded by the user. Each time the user encoded a link/URL, thesystem may identify keywords, phrases and topics for the encoded URLusing any embodiments of the content extractor 315 and/or keywordextractor 515 described herein. In some embodiments, the contentextractor 315 and/or keyword extractor may identify keywords, phrasesand topics for content of or associated with the encoded URL. In someembodiments, the content extractor 315 and/or keyword extractor mayidentify keywords, phrases and topics upon the request to encode the URLor upon encoding the URL. The system may track when the keywords,phrases and/or topics of content of or from URLs that the user requeststo encode and stores this information to the user user's user profile.The system may store this information in correlation with or associationwith the when the user encoded the link, what link was encoded andidentification of the encoded link stored as part of step 760.

At step 765, the system may receive a plurality of click streams thatidentify user actions 250 with the encoded URL encoded by the user. Thesystem, such as via the click tracker, may identify in the click streamsrequests to decode the encoded URL encoded by the user. The system, suchas via the link decoder, may identify and track each time the encodedURL of the user is decoded. The system, such as via the click tracker,may identify the source of the user action to decode the encoded URL.For example, the system may identify the source IP address of thenetwork traffic carrying the user action to decode the encoded URL. Thesystem may identify the web-site, such as the social networking site,from which the network traffic carrying the user action to decode theencoded URL originated. The system may identify the web-site, such asthe social networking site, from which user clicked on the encoded URL.The system may track the number of times the encoded URL of the user hasbeen clicked. The system may track the number of different users thathave clicked the encoded URL of the user. The system may track thenumber of different sources (e.g., web-sites) from which the encoded URLof the user was clicked. The system may track the number of differentgeographic locations from which the encoded URL of the user was clicked.The system may compute any metrics on the information such as averages,peaks, trends, minimums, maximums, etc. The system may store any of theidentified or tracked information and any metrics thereof in the user'suser profile. The system may store any of the identified or trackedinformation and any metrics thereof in the user's user profile incorrelation with or association with any of the information stored inthe user's user profile via steps 760 and/or 765.

At step 770, the influence tracker may determine, generate or otherwiseprovide an influence rating for the user based on the informationidentified and tracked via steps 755, 760 and/or 765. The influencetracker may use the information stored in the user's profile to generatethe influence rating. The influence tracker may receive the informationfrom any one or more of the keyword extractor, click tracker and usertracker to generate the influence rating. The influence tracker maygenerate the influence rating for the user as each click stream for anencoded URL of the user is received and analyzed. The influence trackermay generate the influence rating for any one or more users on apredetermined frequency, such as once per day. The influence tracker maygenerate the influence rating for a user on a per demand basis, such asupon receiving a request for the influence rating of a user specified bya user id. The influence tracker may store the generated influencerating in the user's user profile and update the influenced rating eachtime the influence tracker regenerates the influence rating.

At step 775, the influence rating of the user may influence or affectany relevance scores generated by any embodiments of the systemsdescribed herein. In some embodiments, a relevance based system queriesthe influence tracker to determine the influence rating of a user, suchas via a request and response mechanism. In some embodiments, arelevance based system queries the user profile of a user to determinethe influence rating of the user. In some embodiments, the relevancebased system uses or applies the influence rating as a user weighting indetermining a relevance score. In some embodiments, the relevance basedsystem coverts or transforms the influence rating to a user weighting indetermining a relevance score. In some embodiments, the relevance basedsystem uses the influence rating to up weight or down weight a relevancescore.

H. Systems and Methods for Providing a Recommended List of URLsResponsive to a URL

Referring now to FIGS. 8A and 8B, systems and methods of embodiments ofthe present solution for providing a recommendation list of URLs orlinks given an input or URL link is depicted. Given an input of a linkor URL (e.g., an input link), the present solution may provide an outputof a list of recommended content in ranked order. In some embodiments,the present solution performs these systems and methods without anypersonal or other information about the user—the only input is a URL andthe output is a list of URLs and in some embodiments, also a matching orrecommendation score. The present solution maintains a clickco-occurrence map, sometimes also referred to as click coherency map, tocorrelate all the users that clicked on the input link to the otherlinks which have been clicked on by the users who also clicked on theinput link. The click co-occurrence map may identify the largest numberof those other links to which the users clicked and also clicked on theinput link. The output may be filtered to be domain or content-sitespecific. In other embodiments, raw or unfiltered output may be usefulto aggregators who may provide recommended content from a wider set ofcontent resources.

Referring now to FIG. 8A, an embodiment of a system for providing arecommendation list of URLs or links given an input or URL link isdepicted. In brief overview, the system may include a user tracker 215,click tracker 220 and recommendation system 805. The system may alsoinclude a correlation engine 820 that maintains a click co-occurrencemap 825 from click streams 250. The correlation engine may correlate viathe click co-occurrence map users that clicked on an encoded URL 805 toother encoded URLS those users clicked on 810. The user tracker 215 mayidentify those who users clicked on the encoded URL 805 and who alsoclicked on other encoded URLs. The click tracker may identify the numberof clicks on the encoded URL 805 and for each of the other encoded URLsthe users clicked. The correlation engine may store the clickco-occurrence map into a database 230. The recommendation system 805 mayreceive a URL 205 as input and output a set of URLs based on the clickco-occurrence map 825.

The user tracker may comprise any embodiments of the user tracker 215previously described herein. For any click stream 250′ received by thesystem 120, the user tracker may identify the user who encoded theencoded URL and each of the other users who clicked on the encoded URLof the user. The click tracker may comprise any embodiments of the usertracker 215 previously described herein. For any click stream 250′received by the system 120, the user tracker may identify the number ofusers who encoded the same URL. For any click stream 250′ received bythe system 120, the click tracker may identify the number of users whoclicked on the encoded URL of a user. The click tracker may identify foreach user the number of encoded URLs that user clicked.

Via the user tracker and/or click tracker, the system identifies andtracks the users that clicked on an encoded URL 805. For each usertracked or managed by the system, the system may identify the otherencoded URLs 810 that the same users who clicked the encoded URL 805also clicked. The encoded URL 805 and the other encoded URLs 810 may beencoded URLs of any user. The encoded URL 805 and the other encoded URLs810 may be encoded URLs of the system. The encoded URL 805 and the otherencoded URLs 810 may identify or be associated with any content from anysource. For each encoded URL 805, 810 tracked or managed by the system,the system may identify each of the users the clicked on each encodedURL. The system may identify each user via one or cookies. The systemmay identify each user via information sent with requests to decode theencoded URL. The system may identify each user via the user's accountinformation for the system. In some embodiments, the system tracks theuser id of the user who clicked on the encoded URL without trackingother user information. In some embodiments, the system may identify thesource (e.g., web-site, social networking site, device, etc) from whichthe user clicked on the encoded URL, the time and/or geography fromwhich the user clicked on the encoded URL. The system, such as via usertracker and click tracker, may store the users and number of clicks forthe encoded URLs 805, 810 to the database 230.

The correlation engine 820 may comprise an application, program,library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. Thecorrelation engine may comprise logic, function or operations tocorrelate any URL to a plurality of other URLs. The correlation enginemay comprise logic, function or operations to correlate an encoded URLto a plurality of other encoded URLs. The correlation engine maycomprise logic, function or operations to correlate the users theclicked on encoded URLs 805 to the encoded URLs 810 that the same usersalso clicked. The correlation engine may correlate URLs/encoded URLs toother URLs/encoded URLs based on the number of clicks. In someembodiments, the correlation engine may correlate URLs/encoded URLs toother URLs/encoded URL also based on the source of the clicks, the timeof the clicks and/or the influence of the users or who performed theclicks.

The correlation engine 820 may be designed and constructed to generateand/or maintain a click co-occurrence map 825, sometimes referred to asa map. The click co-occurrence map may comprise any type and form ofdata structure, object, file, table(s) and/or arrangement of data storedin a database. The click co-occurrence map may identify or specify thecorrelation between URLs/encoded URLs to other URLs/encoded URLs. Theclick co-occurrence map may identify or specify the encoded URLs 810 forwhich the users who clicked on the encoded URL 805 also clicked. Foreach encoded URL 810, the click co-occurrence map may identify or trackthe number of such users who clicked both the encoded URL 805 and eachencoded URL 810. For each of the other encoded URLs, the correlationengine may correlate the users and numbers of clicks on these encodedURLS and further encoded URLs those same users also clicked. In someembodiments, the correlation engine may maintain a click co-occurrencemap for a plurality of depths or levels.

In some embodiments, a click co-occurrence map comprises a table, or anyprogrammatic representation thereof, that identifies along one axis,such as the vertical axis, a list of the content the tracked usersinteracted with and along another axis, such as horizontal axis, eachuser. The table may identify or indicate for each user in the horizontalaxis which content in the list of content in the vertical axis that theuser has interacted with (e.g. clicked on). From such a table, thesystem can identify users who have interacted with the same content(e.g., similar interests). The system can also identify for users withsimilar interests what content one user may have interacted with or haveinterest in that the other user has not yet seen or interacted with. Bythe system performing comparison of similarities and differences betweenusers in the map, the system may make recommendations of content for aparticular user.

For each encoded URL 810, the click co-occurrence map may use any numberof thresholds to determine whether or not the other encoded URLs hasbeen clicked enough times or by enough users to be put or maintainedwithin the map. For each encoded URL 810, the click co-occurrence mapmay use any temporal thresholds to determine whether or not the otherencoded URLs 810 are put or maintained within the map. For each encodedURL 810, the click co-occurrence map may use any user influence or userweighting to determine whether or not the other encoded URLs 810 are putor maintained within the map. For each encoded URL 810, the clickco-occurrence map may use any content relevancy score to determinewhether or not the other encoded URLs 810 are put or maintained withinthe map. For each encoded URL 810, the click co-occurrence map may useany trending score to determine whether or not the other encoded URLs810 are put or maintained within the map.

The correlation engine may generate and/or maintain a plurality of clickco-occurrence maps. The correlation engine may generate and/or maintaina click co-occurrence map corresponding to each encoded URL 805. Thecorrelation engine may generate and/or maintain a click co-occurrencemap for a set of encoded URLs 805. The index to a click co-occurrencemap may be an URL or encoded URL. The correlation engine may generateand/or maintain a single click co-occurrence map for all the encodedURLs.

The recommendation system 805 may comprise an application, program,library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. Therecommendation engine may comprise logic, function or operations toprovide a recommendation of one of more other URLs given a URL as input.The recommendation engine may comprise logic, function or operations toanalyze the click co-occurrence map to determine similarities and/ordifference between users in what content users have interacted with orco-clicked and what content users may not have not co-clicked. Therecommendation system 805 may comprise any embodiments of the relevancesystem 405 described herein. The recommendation system may be designedand constructed to receive a URL 205 as input and to produce a set ofone or more URLS 820 as output, such as based on the click co-occurrencemap.

For any output URL, the recommendation system may provide a score, suchas a recommendation or matching score, based on the level of matchingvia the co-occurrence map. The score may indicate or identify the numberof click co-occurrences for the URL in the click co-occurrence map. Therecommendation score may indicate the number of other users with similarclick history or behavior who also clicked on the recommended outputURL. In some embodiments, the recommendation score may be an order orranking of the URL in the list of recommended URLs. In some embodiments,the score may be a relevance score if a user is specified with the URLinput.

The recommendation system may comprise any type and form of interface toreceive a URL 205, such as a graphical user, command line interface orprogrammatic interface. In some embodiments, the programmatic interfaceof the relevance system 405 may comprise an API, web-service orrequest/response mechanism. In some embodiments, the recommendationsystem may receive a plurality of URLs in a single request. In someembodiments, the recommendation system may receive an encoded URL anddecodes the encoded URL.

The recommendation system, such as a responsive to receipt of a URL 205,may be designed and constructed to read, process or query a clickco-occurrence map for or corresponding to the URL. The recommendationsystem may obtain or retrieve via the database a corresponding clickco-occurrence map, such as by using the input URL as an index forretrieval. Via the click co-occurrence map, the recommendation systemmay identify or query the URLs that are mapped to the input URL. Therecommendation system may output the set of mapped URLs 830 via any theinterface, such as displaying via a graphical user interface, output viaa command line interface or via a response of a programmatic interface.The recommendation system may rank the mapped URLs by the number ofclicks on the mapped URLs. The recommendation system may output anenumeration of URLs 830 by ranking.

The recommendation system may filter any of the ranked or unranked URLs830 based on a number of clicks threshold. The recommendation system mayfilter any of the ranked or unranked URLs 830 based on domain filtering.The recommendation system may filter any of the ranked or unranked URLs830 using content based or content specific filtering. In someembodiments, if a user is also specified with the URL input, therecommendation system may also provide a relevance score for each of theURLs 830 by using any of the relevance scoring systems, methods andtechniques previously described herein.

Referring now to FIG. 8B, an embodiment of a method for providing arecommendation list of URLs or links given an input or URL link isdepicted. In brief overview, at step 855, the system, such as anyembodiments of linking system 120, identifies the users who clicked onan encoded URL. At step 860, the system identifies the plurality ofother encoded URLs those users clicked on. At step 865, the systemgenerates a click co-occurrence map, such as to correlate the encodedURL of step 855 to the plurality of other encoded URLs clicked by eachuser at step 860 who also clicked the encoded URL. At step 870, thesystem received a URL as input and at step 875, as output, the systemprovides an enumeration of recommended URLs based on a clickco-occurrence map. In some embodiments, the output may also include aranking and/or recommendation score.

At step 855, the system identifies each of the users who clicked on anencoded URL 805, requested to decode an encoded URL 805 or otherwiseinteracted with the encoded URL or corresponding URL. For each clickstream and/or decoding of the encoded URL, the system may track for eachencoded URL the users who clicked on the encoded URL. The system mayidentify each user via a user id 345. In some embodiments, the systemidentifies the user via an account or login of the linking system 120.In some embodiments, the system identifies the user via a cookie 255.The system such as via the link encoder and user tracker may track eachinstance of the user decoding an encoded link and store that informationto the database 230 and/or to the user's profile 710. The system maytrack when the user decoded the link, what encoded link was decoded andidentification of the link and store this information to the database,such as in the user's user profile.

At step 860, for each of the users identified as clicking on the encodedURL 805, the system identifies the other encoded URLs 810 those usersalso clicked or have clicked. The system via user tracker and clicktracker may store in the database a history of encoded URLs each of theusers have previously clicked on. The system may also continue to track,such as in real-time, the encoded URLs each of the users are clickingon. For each click stream and/or decoding of an encoded URL, the systemmay track by user the encoded URLs that user has clicked on. The systemmay track and store in the user's user profile any informationidentifying the encoded URLs/URLs that user has clicked on or otherwiseinteracted with.

At step 865, the system correlates the user and click trackinginformation, such as the user and click information obtained via steps855 and 860 to generate and/or maintain one or more click co-occurrencemaps. For each encoded URL 805 and for each of the users that clicked onor interacted with each encoded URL 805, the system may identify theother encoded URLs 810 that user also clicked on or interacted with. Foreach encoded URL 810, the click co-occurrence map may identify or trackthe number of such users who clicked both the encoded URL 805 and eachencoded URL 810. The system may maintain a click co-occurrence map tocorrelate each encoded URL/URL 805 to the other encoded URLs/URLs 810.The system may maintain a separate click co-occurrence map for eachencoded URL/URL. The system may maintain a click co-occurrence map forall encoded URL/URLs that is indexed by or organized by encoded URL. Thesystem may update the click co-occurrence map upon processing each clickstream. The system may update the click co-occurrence map upon decodingeach encoded URL.

At step 870, the system receives an input identifying or comprising aURL. The system may receive the input via a request. The system mayreceive the URL as input via a graphical user interface. The system mayreceive the URL as input via a command line interface. The system mayreceive the URL as input via a programmatic interface. For example, thesystem may receive the URL as input via an API call, such as a webservice call. The system may receive the URL as input via an HTTPrequest. The system may receive the URL as input from any component ofthe linking system 120. The system may receive the URL as input from anythird party system, including applications, servers and systems. Thesystem may receive the URL as input from a web page, script or otherexecutable instructions executing on a web page or web server. Thesystem may receive the URL as input from any type and form of mobileapplication executing on a mobile device, such as a smart phone.

The system may also receive with the URL input or otherwise associatedwith a request, any one or more parameters for filtering the output.These parameters may include geography information. These parameters mayinclude language information or identification. These parameters mayinclude identification or specification of domain filters and/or contentfilters. These parameters may include identification or specification ofany thresholds, such as number of recommended URLs. These parameters mayinclude identification or specification of a ranking or ordering of theoutput. These parameters may include identification or specification ofproviding or generating a score for the URLs in output, such as usingany of the relevance scoring techniques described herein.

At step 875, the system provides as output 830 one or more URLs. Thesystem may provide the output responsive to receipt of the URL 805 asinput. The system may use the input URL to retrieve, lookup, query orobtain a click co-occurrence map, such as from database 230, for orcorresponding to the input URL. Via the click co-occurrence map, thesystem may identify a plurality of encoded URLs clicked on by users whoalso clicked on the input URL. Via the click co-occurrence map, thesystem may determine a plurality of encoded URLs clicked on by thelargest number of users who also clicked on the input URL. The systemmay provide the output 830 via a graphical user interface or commandline interface. The system may provide the output 830 as a response to arequest, such as an HTTP response to an HTTP request. The system mayprovide the output 830 as a response or data structure to an API call.The system may provide the output as an enumerated list of URLs. Thesystem may provide the output as an enumerated list of URLs ranked inorder based on number of user interactions/clicked and/or relevance.

The system may apply any filtering, such as domain or content filtering,based on any filtering parameters specified via the input or request.For example, the system may exclude URLs from the output that match orcorrespond to a domain. In another example, the system may exclude URLsfrom the output that link to or comprise content corresponding to orhaving keywords corresponding to specified content. The system may applyany threshold based on any threshold parameters specified via the inputor request. For example, the system may provide as output a top 10ranking of URLs. In another example, the system may provide an output ofURLs that had at least a predetermined number of clicks (e.g., 100,1000, etc). As may be requested via the input or the request orotherwise as a default, the system may determine or provide a score witheach URL 830 in the output. The system may give each URL in the output ascore based on the level or degree of matching or recommendation fromanalysis of the click co-occurrence map. If a user is specified with theinput URL, the system may give each URL in the output a score based onany of the relevance scoring techniques described herein.

I. Systems and Methods for Monitoring Online Activity Via Cookies of aLinking System

Referring now to FIGS. 9A and 9B, systems and methods of monitoringonline activities of users via cookies of a linking system is depicted.A linking system configured to generate links to content can generateand transmit cookies to client devices responsive to receivinginteractions (e.g., clicks) from the client devices. The first time aclient device requests access to content via a link generated by thelinking system, the linking system can provide a response to the requestthat includes a cookie generated specifically for the client device fromwhich the request was received. The cookie is stored on the clientdevice, for instance, in a folder accessible by the browser of theclient device. Each time a subsequent request to access content via alink of the linking system is received from the client device, therequest will include the cookie. Each time the linking system receivesthe cookie, the linking system updates the cookie to include informationcorresponding to the request. For instance, the linking system canupdate the cookie to include a source URL from which the request wasreceived and a destination URL identifying the URL of the content linkedto the link. By updating the cookie each time the client device requeststo access content via a link of the linking system, the cookie canmaintain a history of source resources (e.g., URLs) from which requeststo access content by the client device have been received by the linkingsystem as well as destination resources (e.g., URLs) that include thecontent to which the client device requested access.

Stated in another way, the present solution involves a linking systemthat utilizes a cookie to track or monitor the online activity of aclient device via interactions (e.g., clicks) on links encoded by thelinking system. The encoded links are displayed on source resources(e.g., source URLs) and linked to destination resources (e.g.destination URLs). The cookie may allow a linking system to integrate,for the cookie, all interactions (e.g., clicks) on links encoded by thelinking system performed via the client device. With this configuration,the linking system may collect, via a cookie of the linking system thatis assigned to a client device of a user, browsing activity acrossresources of various domains and determine, from the collected useractivity, all of the resources accessed by the client device. Thelinking system can then use the data collected for each client device(or browser of a client device) assigned a cookie to better understandrelationships between different resources based on the browsingactivities of various client devices.

According to some aspects, an online activity monitoring system, such asthe linking system, can receive a first request from a browser of aclient device to access a first destination URL via a link encoded bythe linking system that was provided for display on a first source URL.The online activity monitoring system can generate a cookie for theclient device and update the cookie to include data identifying thefirst source URL and the first destination URL. The online activitymonitoring system can then provide the cookie to the browser of theclient device. The online activity monitoring system may receive asecond request from the browser of the client device to access a seconddestination URL via a second link encoded by the linking system that wasprovided for display on a second source URL. The second request from thebrowser can include the cookie previously provided by the onlineactivity monitoring system. The online activity monitoring system canupdate the cookie to include data identifying the second source URL andthe second destination URL such that the cookie has information on thefirst source URL, the second source URL, the first destination URL andthe second destination URL. Each of the URLs can correspond to the sameor different domains. The online activity monitoring system can thenprovide the updated cookie to the client device. The online activitymonitoring system can provide the updated cookie within a response tothe request. As the client device requests to access additional contentvia links encoded by the linking system, the cookie assigned to theclient device is updated such that the cookie includes data identifyinga plurality of resources visited by the client device.

Referring now to FIG. 9A, an embodiment of an online activity monitoringsystem 900 for monitoring online activities of client devices viacookies is depicted. In brief overview, the online activity monitoringsystem 900 can be a part of the linking system 120 or may includecomponents and functionality of the linking system 120, as shown inFIGS. 2A-8A. The online activity monitoring system 900 may include acookie management engine 902 and a resource identification engine 908.The online activity monitoring system 900 may be included in a linkingsystem 120 and may execute on one or more server(s) 106A-N (see FIG. 2).The cookie management engine 902 may include a cookie generator 904 thatgenerates a cookie (e.g., an HTTP cookie) responsive to a request toaccess a resource from a browser of a client device through the network104. In some embodiments, the request can be generated responsive to anaction taken on an encoded link generated by the linking system 120 thatis included within a resource, such as the first resource 950 a andprovided for display on the client device. The encoded link can belinked to another resource, such as the second resource 950 b. Thecookie management engine 902 also includes a cookie updater 906 thatupdates the same cookie responsive to a subsequent request to access aresource by the same browser of the client device.

The online activity monitoring system 900 may provide the generated orupdated cookie to the browser of the client device via the network 104.Here, the client device 102 a and 102 b may be either local machines,client nodes, client machines, client computers, endpoints, or endpointnodes in communication with one or more servers 106 a-106 n via one ormore networks 104 (see also FIG. 1A). In some embodiments, the clientdevice 102 a and 102 b may have the capacity to function as both aclient node seeking access to resources provided by a server and as aserver providing access to hosted resources for other clients 102 a-102n (see also FIG. 1A). Also, the linking system 120 may be configured toshorten, share and track links, and execute on one or more server(s)106A-N and may be accessed by a plurality of clients 102 a-102 n via thenetwork 104 (see also FIG. 2).

For example, referring to FIG. 9A, the linking system 120 can generate afirst encoded URL 914 a linked to a second resource 950 b. The linkingsystem 120 can also generate a second encoded URL 914 b linked to athird resource 950. The linking system 120 can further generate a thirdencoded URL 914 c linked to the second resource 950 b. In someembodiments, the encoded URLs can be included in source resources bycontent publishers so they can monitor performance or behaviors of usersby integrating the users' interactions with the encoded URLs viacookies. In some implementations, the linking system 120 can receive arequest to generate an encoded link for a particular resource. Therequest can be initiated at a device of a client or a content publisher.In some implementations, the request can include a URL of the particularresource for which the encoded link is to be generated. The encodedlink, as described herein, when clicked or otherwise interacted on at aclient device, can cause the client device to send a request to thelinking system 120, which in turn, will send back a response to redirectthe client device to the particular resource.

For example, a first resource 950 a can include a first encoded URL 914a that is generated by the linking system 120 and that is linked to asecond resource 950 b. Responsive to an action taken on the firstencoded URL 914 a, the client device 102 a may generate and transmit arequest 920 to the online activity monitoring system 900 to access thesecond resource 950 b. The online activity monitoring system 900 mayreceive the request 920 and responsive to receiving the request 920, theresource identification engine 908 may identify from the request 920,the identity of the first resource 950 a and the second resource 950 b.

The online activity monitoring system 900 can be configured to determinewhether the request 920 includes a cookie of the linking system 120. Insome implementations, the cookie generator 904 may determine whether therequest 920 includes a cookie of the linking system 120. In someembodiments, the cookie generator 904 can generate a cookie 955 aresponsive to determining that the request 900 did not include a cookieof the linking system 120. In some embodiments, the cookie generator 904can determine if the online activity monitoring system 900 did notpreviously assign a cookie to the browser of the client device fromwhich the request was received. Responsive to determining that theonline activity monitoring system 900 did not previously assign a cookieto the browser of the client device from which the request was received,the cookie generator 904 can generate a cookie to provide to the clientdevice.

The cookie generator 904 can generate the cookie and include informationin the cookie that indicates that the browser of the client device 102 ahas accessed the first resource 950 a and the second resource 950 bresponsive to receiving the request 920. The online activity monitoringsystem 900 can be configured to generate and transmit, to the clientdevice 102 a, a response 924 including the generated cookie 955 a and afirst redirect link 926. The first redirect link is configured to causethe browser 912 a of the client device to be redirected from the firstresource 950 a to the second resource 950 b. In some embodiments, uponreceiving the response 924, the browser 912 a of the client device 102 amay store, in memory of the client device, the cookie 955 a included inthe response 924. At this point and therefrom, the resourceidentification engine 908 may identify, via the cookie 955 a, that theclient device 102 a has accessed the first resource 950 a and the secondresource 950 b.

Next, responsive to an action taken on the second encoded URL 914 b thatis linked to the third resource 950 c, the client device 102 a cangenerate a request 930 to access the third resource 950 c. The requestcan include the cookie previously provided by the linking system 120 andstored in memory of the client device, for instance, in a cache of thebrowser of the client device. The online activity monitoring system 900can receive the request 930. Responsive to receiving the request 930,the resource identification engine 908 can be configured to identify,from the request 930, the cookie 955 a, the third resource 950 c and thefourth resource. The fourth resource can be one of the first resource,the second resource or any other resource. In addition, the cookieupdater 906 can be configured to update the cookie 955 a to includeinformation indicating that the client device 102 a has accessed thefirst resource 950 a, the second resource 950 b, the third resource 950c and the fourth resource. The cookie updater 906 can store theinformation in the cookie 955 a to generate an updated version of thecookie 955 a′. The online activity monitoring system 900 can beconfigured to then send back to the client device 102 a, a response 934that includes the updated cookie 955 a′ and a second redirect link 936that will redirect the browser 912 a from the fourth resource to thethird resource 950 c. In some embodiments, upon receiving the response934, the browser 912 a of the client device 102 a can update the storedcookie 955 a to the updated cookie 955 a′ included in the response 934.At this point and therefrom, the resource identification engine 908 orthe online activity monitoring system 900 may identify, via the cookie955 a′ that the client device 102 a has accessed the first resource 950a, the second resource 950 b, the third resource 950 c and the fourthresource.

Similarly, responsive to a subsequent action (e.g., clicks or otherinteractions) performed on any URL (not shown) encoded by the linkingsystem 120 that is included in a fifth resource (not shown) and linkedto a sixth resource (not shown), the client device 102 a can generate athird request (not shown) including the cookie 955 a′ to the system 900to access the sixth resource. The online activity monitoring system 900may receive the third request. Responsive to the third request, theresource identification engine 908 may identify from the third request,the updated cookie 955 a′, the fifth resource and the sixth resource. Inaddition, the cookie updater 906 may further update the cookie 955 a′ toa further updated cookie (not shown). The further updated cookie mayindicate that the client device 102 a has accessed the first resource950 a, the second resource 950 b, the third resource 950 c, the fourthresource, the fifth resource and the sixth resource. The system 900 canthen send back to the client device 102 a a further response (not shown)that now includes the further updated cookie and a further redirect link(not shown). The further redirect link can cause the browser 912 a to beredirected from the fifth resource to the sixth resource. At this pointand therefrom, the resource identification engine 908 may identify viathe further updated cookie that the client device 102 a has accessed thefirst resource 950 a, the second resource 950 b, the third resource 950c, the fourth resource, the fifth resource and the sixth resource.

Furthermore, referring to FIG. 9A, for example, responsive to an action(e.g., clicks or other interactions by using a browser 912 b in theclient device 102 b) performed on the third encoded URL 914 c that islinked to the second resource 950 b, the client device 102 b may providea request 940 to the system 900 to access the second resource 950 b. Thesystem 900 may receive the request 940 and responsive to the request940, the resource identification engine 908 may identify, from therequest 940, the second resource 950 b. The cookie generator 904 can beconfigured to generate a cookie 955 b indicating that the client device102 b has accessed the second resource 950 b. The online activitymonitoring system 900 can be configured to generate and transmit, to theclient device 102 b, a response 944 including the generated cookie 955 band a third redirect link 946. The third redirect link 946 can beconfigured to cause the browser 912 b to the second resource 950 b. Insome embodiments, upon receiving the response 944, the browser 912 b ofthe client device 102 b may store, in memory, the cookie 955 b includedin the response 944. At this point and therefrom, the resourceidentification engine 908 may identify via the cookie 955 b that theclient device 102 b has accessed the second resource 950 b. It should beappreciated that each cookie and any updated versions of the cookie canbe unique to a particular client device. In some embodiments, eachcookie and any updated versions of the cookie can be unique to aparticular browser or application of the client device.

The cookie management engine 902 may comprise an application, program,library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. The cookiemanagement engine 902 may comprise logic, function or operations tocreate a cookie relating to a particular client device or application orprogram (e.g., browser) in a client device, and store, update andretrieve the cookie. More particularly, the cookie generator 904 maycomprise logic, function or operations to create a cookie relating tothe particular client device or application or program in a clientdevice, and the cookie generator 906 may comprise logic, function oroperations to update the cookie. The cookie management engine 902 maycomprise logic, function or operations to create a plurality of cookiesrelating to respective client devices or applications or programs (e.g.,browsers) in a client device, and store, update and retrieve theplurality of cookies.

The cookie management engine 902 may comprise logic, function oroperations to correlate a client device or application or program to aplurality of source resources and destination resources to which thesame client device or application or program has provided requests toaccess resources linked to encoded links generated by the linking system120. In some embodiments, to perform such correlations, the cookiemanagement engine 902 may comprise the link decoder 212 configured todecode an encoded URL, such as via the database 230 and perform a lookupof the URL corresponding to the shortened or encoded URL (see FIG. 2).The cookie management engine 902 may also correlate the client devicesor applications or programs to source resources from which the sameclient device or application or program has generated request to accessanother resource (by clicking encoded links on those source resources byusers of the client devices or applications or programs).

In some embodiments, to perform such correlations, the cookie managementengine 902 may comprise logic, function or operations to identify sourceresource information from a header of a request generated by a clientdevice (e.g., the HTTP header field of referrer). For example, referringto FIG. 9A, responsive to the request 920, the cookie generator 904 maygenerate the cookie 955 a correlating the client device 102 a to boththe first resource 950 a, from which the client device 102 a generated arequest to access the second resource 950 b, and the second resource 950b. Moreover, referring to FIG. 9A, responsive to the request 930 made bythe same client device 102 a, the cookie updater 906 may update the samecookie 955 a to an updated cookie 955 a′ so as to correlate the clientdevice 102 a not only to the first resource 950 a and the fourthresource, from which the client device 102 a has generated a request toaccess other resources, but also to the second resource 950 b and thethird resource 950 c, to which the encoded links were linked.Furthermore, referring to FIG. 9A, responsive to the request 940generated by a different client device, i.e., the client device 102 b,the cookie generator 904 may generate a cookie different from the cookie955 a associated to the client device 102 a, i.e., the cookie 955 b, tocorrelate the client device 102 b to the second resource 950 b to whichthe client device 102 b generated request to access.

In some embodiments, if the online activity monitoring system 900receives a first request from a first client device and determines thatthe first request does not include any cookie, the cookie generator 904may create or generate a cookie and provide the cookie to the firstclient device. Subsequently, if the online activity monitoring system900 a second request from the first client device and identifies acookie of the linking system 120 included in the second request, theonline activity monitoring system 900, via the cookie updater 906, mayupdate browsing data stored in the cookie to include a source resourceand a destination resource identified by the second request, andtransmit the updated cookie to the first client device.

Similarly, the online activity monitoring system 900 may provide, viathe cookie generator 904, a second cookie to a second client device thatis different from the first client device, responsive to receiving fromthe second client device, a request to access any link encoded by thelink shortening system 120.

The resource identification engine 908 may comprise an application,program, library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. Theresource identification engine 908 may comprise logic, function oroperations to identify from a request (e.g., an HTTP request) generatedby a client device, both a source resource, from which the client devicegenerated request to access to a destination resource (by clicking anencoded link liked to the destination resource), and the destinationresource. In some embodiments, the resource identification engine 908may comprise logic, function or operations to identify the sourceresource information from a header of the request (e.g., the HTTP headerfield of referrer) and identify the destination resource informationusing the link decoder 212 of the linking system 120 configured todecode an encoded URL, such as via the database 230 and perform a lookupof the URL corresponding to the shortened or encoded URL (see FIG. 2).

The resource identification engine 908 may also comprise logic, functionor operations to identify from a request generated by a client deviceand a cookie included therein, both a source resource, from which theclient device generated the request to access to a destination resource(by clicking an encoded link liked to the destination resource), and thedestination resource. The resource identification engine 908 may alsocomprise logic, function or operations to identify from a requestgenerated by a client device and a cookie included therein, both aplurality of source resources, from which the client device haspreviously generated requests to access to a plurality of destinationresources, and the plurality of destination resources. For example,referring to FIG. 9A, responsive to the request 930 generated by theclient device 102 a, the resource identification engine 908 may identifyfrom the request 930 and the cookie 955 a included therein, both aplurality of source resources (e.g., the first resource 950 a), fromwhich the client device has previously generated requests to access to aplurality of destination resources (e.g., the second resource 950 b andthe third resource 950 c), and the plurality of destination resources.

In other embodiments, the online activity monitoring system 900 mayprovide an output identifying a plurality of resources accessed by theclient device. The online activity monitoring system 900 may identify,from a cookie relating to a particular client device, a plurality ofresources accessed by the particular client device based on linksencoded by the link shortening system. For example, the online activitymonitoring system 900 may identify, from the cookie 955 a relating tothe client device 102 a, a plurality of resources (e.g., the secondresource 950 b and the third resource 950 c) accessed by the clientdevice 102 a based on the encoded links 1 and 2 (914 a and 914 b).

The online activity monitoring system 900 can be configured to maintaincopies of cookies provided to each of the client devices that accesslinks encoded by the online activity monitoring system 900. In someembodiments, the online activity monitoring system 900 can store, inmemory, a cookie of the each of the cookies provided by the onlineactivity monitoring system 900 and can be configured to update thecookie stored in the memory each time the cookie is updated by thelinking system. The cookies can identify the source URLs from whichbrowsers of client devices can access links encoded by the onlineactivity monitoring system 900 as well as the destination URLs to whichto redirect the browsers responsive to accessing the encoded links.

The online activity monitoring system 900 can be configured to analyzethe resources identified by the cookies of the linking system 120 thatare provided to browsers or applications of client devices to identifyone or more relationships between resources. For instance, the onlineactivity monitoring system 900 can determine, from the plurality ofcookies of the linking system 120, that a large number of users visiteda first resource and a second resource that is not linked to the firstresource by an encoded link of the linking system 900. The onlineactivity monitoring system 900 can determine that there is a correlationbetween the first resource and the second resource by comparing thenumber of cookies that identified both the first resource and the secondresource to a predetermined threshold value. For instance, if more than10% of the cookies corresponding to client devices visited a firstresource and a second resource, the online activity monitoring system900 can determine that the first resource and the second resourceattract similar types of users.

In some implementations, the online activity monitoring system 900 cangenerate reports based on trends identifiable from the history ofresources accessed by the each of the cookies of the linking system 120.The online activity monitoring system 900 can further provide thegenerated reports to entities requesting such reports.

In some embodiments, the online activity monitoring system 900 canidentify, from cookies relating to particular client devices or a cookiehistory of the client devices, that many of the client devices whichvisited resources in a first domain generated requests to accessresources in a second domain. In this manner, the online activitymonitoring system can identify correlation between different domainsbased on the online activity of multiple client devices. In someembodiments, the online activity monitoring system 900 can identify, foreach domain, a list of other domains that are related via cookiehistory, and determine the top domains that are visited from eachdomain.

Referring now to FIG. 9B, an embodiment of a method for monitoringonline activities of users via cookies is depicted. In brief overview,at step 970, the system or server, such as any embodiments of the onlineactivity monitoring system 900, receives from a client device, a firstrequest from a first resource to access a first link that is encoded bya linking system and linked to a second resource. At step 975, theonline activity monitoring system 900 provides a cookie of the linkingsystem to the client device responsive to receiving the first requestfrom the client device. The online activity monitoring system 900 mayidentify, from the cookie of the link shortening system, the firstresource and the second resource. At step 980, the online activitymonitoring system 900 receives from the client device, a second requestto access a second link that is encoded by the linking system and linkedto a third resource. At step 985, the system identifies from the secondrequest, the same cookie provided to the client device and the thirdresource. At step 990, the online activity monitoring system 900identifies, via the cookie provided to the client device, that theclient device has accessed the first resource, the second resource andthe third resource.

In further detail, at step 970, the online activity monitoring system900 may receive from a client device (e.g., the client device 102 a or102 b) a first request from a first resource to access a first link thatis encoded by the linking system (e.g., the linking system 120) andlinked to a second resource. Referring to FIG. 9A, for example, when auser of the client device 102 a clicks or otherwise interacts with(e.g., by using the browser 912 a in the client device 102 a) the firstencoded URL 914 a on the first resource 950 a that is linked to thesecond resource 950 b, the client device 102 a may provide a request 920to the system 900 to access the second resource 950 b and the system 900may receive the request 920. The first encoded URL 914 a can be includedwithin the first resource such that when the contents of the firstresource is displayed on a client device via the browser 912 a, a userof the client device 102 a can click or otherwise interact with theencoded URL 914 a.

In some embodiments, the browser executing on a client device mayinclude a web browser configured to retrieve, present and traverseinformation resources on the World Wide Web via the Internet. In someother embodiments, the browser may comprise an application, program,library, service, script, process, task or any type and form ofexecutable instructions executing or executable on the client device.The browser can be configured to retrieve, present and traverseinformation resources that are linked to each other by hypertext links.In some embodiments, when a user visits a first page that includes alink encoded by the linking system, responsive to the user's access tothe encoded link, a browser may provide, to the online activitymonitoring system, a request including information identifying thesource resource (i.e., the resource on which the encoded link wasaccessed). The user may access the encoded link by clicking on the linkor performing one or more other qualifying interactions. Suchinteractions may include tapping on a touchpad or trackpad, pressing atouch screen with a finger or tool, or eye movements by use of eyetracking devices. In some embodiments, the browser may generate an HTTPrequest message that includes a source resource URL (i.e., the URL ofthe first page) and a destination resource URL which is the address ofthe encoded link.

At step 975, the online activity monitoring system 900 may provide acookie of the linking system to the client device responsive toreceiving, from the client device, the first request to access the firstencoded link that is linked to the second resource. Referring to FIG.9A, for example, responsive to the first request 920, the resourceidentification engine 908 may identify from the first request 920, thefirst resource 950 a and the second resource 950 b by using a header ofthe first request (e.g., the HTTP header field of referrer) and the linkdecoder 212 configured to decode the first encoded URL 914 a, such asvia database 230 and perform a lookup of the URL corresponding to theencoded URL (see FIG. 2). Next, the cookie generator 904 may generatethe cookie 955 a indicating that the client device 102 a has accessedthe first resource 950 a and the second resource 950 b. In someimplementations, the cookie generator 904 can maintain, in memory, anindication of the cookie generated by the online activity monitoringsystem 900 and store a copy of the cookie provided to the client devicein memory. The cookie can include information that identifies the sourceresource URL identifying the first resource and the destination resourceURL identifying the second resource.

The online activity monitoring system 900 may then send back, to theclient device 102 a, the response 924 (e.g., an HTTP response) includingthe generated cookie 955 a and a first redirect link 926. The firstredirect link 926 can redirect the browser of the client device from thefirst resource 950 a to the second resource 950 b. In some embodiments,the system may provide the cookie responsive to determining that thefirst request does not identify a cookie. For example, if the system 900receives the first request from a first client device and determinesthat the first request does not include a cookie, the cookie generator904 may create or generate a cookie and provide it to the first clientdevice.

At step 980, the online activity monitoring system 900 may receive fromthe client device, a second request to access a second link that isencoded by the linking system and linked to a third resource. Referringto FIG. 9A, for example, the user or another user of the client device102 a may click the second encoded URL 914 b on the fourth resource (notshown), that is linked to the third resource 950 c. The client device102 a may generate the second request 930 including the cookie 955 a tothe system 900 to access the third resource 950 c in response toreceiving an indication of an action performed on the second encoded URL914 b. In some embodiments, the browser of the client device on whichthe action was performed can store, in memory of the client device 102,a copy of the cookie received from the linking system. The browser canbe configured to generate the second request to provide to the linkingsystem 900. The browser can include a copy of the cookie received fromthe linking system in the second request, which can also identify theURL on which the action was performed.

At step 985, the online activity monitoring system 900 may identify fromthe second request, the same cookie provided to the client device andthe third resource. In some embodiments, the online activity monitoringsystem 900 may identify from the second request, the fourth resourcefrom which the online activity monitoring system 900 received the secondrequest. Referring to FIG. 9A, for example, responsive to the request930, the resource identification engine 908 may identify, from therequest 930, the cookie 955 a previously provided by the online activitymonitoring system 900 to the first client device 102 a, the thirdresource 950 c and the fourth resource (source resource). The cookieupdater 906 can then update the cookie 955 a included in the secondrequest that is received from the client device to indicate that theclient device 102 a has accessed the first resource 950 a, the secondresource 950 b, the third resource 950 c and the fourth resource. Theonline activity monitoring system 900 may then send back to the clientdevice 102 a the response 934 that now includes the updated cookie 955a′ and a second redirect link 936. The second redirect link will thenredirect the browser from the fourth resource to the third resource 950c.

In some embodiments, the fourth resource can be the same as the firstresource, e.g., the first resource 950 a. As described herein, the firstrequest may be received by receiving a first interaction on the firstencoded link, and the second request may be received by receiving asecond interaction on the second encoded link. Referring to FIG. 9A, forexample, the request 920 is received by receiving a first click by auser of the client device 102 a on the first encoded URL 914 a, and therequest 930 is received by receiving a second click by a user of theclient device 102 a on the second encoded URL 914 b.

In some implementations, the online activity monitoring system 900 canstore, in memory of the linking system 900, a copy of the updated cookie955′. In some implementations, the online activity monitoring system 900can maintain, in memory, a record of the resources accessed by thebrowser of the client device. Each time the browser of the client devicetransmits a request to the online activity monitoring system 900responsive to an action performed on a link encoded by the linkingsystem, the cookie of the online activity monitoring system 900 providedto the browser can be updated to identify the source resource on whichthe action was performed and the destination resource to which theencoded link is configured to redirect the browser.

At step 990, the online activity monitoring system 900 may identify, viathe cookie provided to the client device, that the client device hasaccessed the first resource, the second resource and the third resource.Referring back to FIG. 9A, for example, after sending the response 934back to the client device 102 a, the resource identification engine 908may identify via the cookie 955 a′ that the client device 102 a hasaccessed the first resource 950 a, the second resource 950 b, the thirdresource 950 c and the fourth resource.

In some embodiments, the online activity monitoring system 900 mayfurther receive a third request from a fifth resource to access a linkthat is encoded by the linking system and linked to a sixth resource,and identify, via the cookie provided to the client device, that theclient device has accessed the first resource, the second resource, thethird resource, the fourth resource, the fifth resource and the sixthresource.

In some embodiments, the online activity monitoring system 900 mayprovide an output identifying a plurality of resources accessed by theclient device. In some embodiments, responsive to the online activitymonitoring system 900 receiving the second request from the clientdevice, the online activity monitoring system 900 may receive the cookieincluded in the second request, update browsing data stored in thecookie to include a source resource and a destination resourceidentified by the second request, and transmit the updated cookie to theclient device.

In some embodiments, the online activity monitoring system 900 mayprovide a second cookie to a second client device responsive toreceiving a request to access any link encoded by the linking system.Referring to FIG. 9A, for example, the online activity monitoring system900 may provide the second cookie (e.g., the cookie 955 b) to the secondclient device (e.g., the client device 102 b) that is different from thefirst client device (e.g., the client device 102 a), responsive toreceiving a request (e.g., the request 940) to access any link encodedby the linking system 120 (e.g., the third encoded URL 914 c). In someembodiments, the online activity monitoring system 900 may identify fromthe second cookie, a plurality of resources accessed by the secondclient device based on links encoded by the link shortening system.

J. Systems and Methods for Analyzing Traffic Across Media Channels ViaEncoded Links

Referring now to FIGS. 10A and 10B, systems and methods of analyzingtraffic across multiple media channels via encoded links is depicted.The present solution may generate respective encoded links for differentmedia channels, determine, for each of the media channels, statisticsrelated to user interactions with the encoded links, and providechannel-specific or aggregate information based on the statistics. Therespective encoded links for the media channels may allow a mediachannel analysis system 1000 to track statistics for each media channeland provide aggregate or channel-specific information based on thestatistics. For example, a marketer may use the media channel analysissystem to evaluate the performance of their marketing campaigns acrossmultiple marketing channels including dark social media (e.g., email andShort Message Service (SMS)). Responsive to receiving a request from amarketer to shorten a link to content (for example, an informationresource such as a webpage), the media channel analysis system cangenerate multiple links to the content. Each of the generated links canbe specific to a particular marketing channel. The system can then trackstatistics for each media channel based on activity tracked on each ofthe encoded links and provide both aggregate and channel-specificinsights to the marketer.

Referring to FIG. 10A, the media channel analysis system 1000 can beincluded in or executed on one or more servers 106A-N. The media channelanalysis system 1000 can include one or more components, modules,scripts, programs, or set of instructions. The media channel analysissystem 1000 can include an encoded link generator 1015 and a trafficanalyzer 1025. The media channel analysis system can be a part of alinking system, such as the linking system 120 described herein withrespect to FIGS. 2A-10B.

The encoded link generator 1015 can be configured, constructed ordesigned to receive identification of a plurality of media channels forwhich to generate encoded links to a resource. In some embodiments, theencoded link generator 1015 can receive a request, from a client deviceof a marketer or advertiser, to generate one or more media channelspecific encoded links to a resource, such as a resource 1040. Theresource 1040 can be an information resource, such as a web page. Theresource 1040 can be stored on a server and may correspond to or beaccessed via a resource URL 1045. In some embodiments, the encoded linkgenerator 1015 can be configured to provide a user interface to a clientdevice through which one or more requests to generate encoded links canbe received. The user interface can provide one or more input fields.Via the user interface, a user of the client device can provide aresource URL, such as the resource URL 1045 of the resource 1040, forwhich to generate one or more media channel specific encoded links. Theuser of the client device can also provide, via the user interface, anidentification of one or more media channels for which to generateencoded links to the resource. For instance, the user of the clientdevice can provide a URL of the resource and an indication of a socialnetwork media channel, a SMS media channel and an email media channelfor which to generate specific encoded links. Each of the specificencoded links is linked to the same resource. In some embodiments, therequest can identify a first media channel 1050 a and a second mediachannel 1050 b for which to generate media channel specific encodedlinks.

The encoded link generator 1015 can be configured to generate differentencoded links to the resource for each of the different media channelsfor which an encoded link is to be generated. The encoded link generator1015 can be configured to generate a first encoded media channel link1055 a and a second encoded media channel link 1055 b both of which arelinked to the same resource 1040. The first encoded media channel link1055 a can correspond to the first media channel 1050 a, while thesecond encoded media channel link 1055 b can correspond to the secondmedia channel 1050 b identified in the request received from the clientdevice. The request can identify one or more additional media channelsfor which to generate media channel specific encoded links. A mediachannel specific encoded link can be a link to the resource that can beassigned to or otherwise attributed or associated with a particularmedia channel. A media channel can be a channel for which statisticsrelating to online activity may be determined. Examples of mediachannels can include one or more of the following: a social mediaplatform, such as FACEBOOK, TWITTER, INSTAGRAM, an email channel, an SMSchannel, a particular website or domain (for instance, YAHOO.COM,AOL.COM, etc.), or one or more websites belonging to a particularcategory, such as sports or music or news, among others.

The encoded link generator 1015 can be configured to generate the firstencoded link 1055 a. The first encoded link can be configured such thatwhen a client device accesses the first encoded link, the client devicesends a request to a server of the media channel analysis system 1000,which then redirects the client device to a server of the resource towhich the first encoded link is linked.

The encoded link generator 1015 can be configured to associate a mediachannel identifier (ID) of the first media channel 1050 a to thegenerated first encoded link 1055 a. In this way, any activity performedvia the first encoded link can be attributed to the first media channel1050 a. Similarly, the encoded link generator 1015 can also beconfigured to generate the second encoded link 1055 b. The secondencoded link 1055 b is linked to the same resource as the first encodedlink 1055 a. The second encoded link can be configured such that when aclient device accesses the second encoded link, the client device sendsa request to a server of the media channel analysis system 1000, whichthen redirects the client device to a server of the resource to whichthe second encoded link is also linked.

The encoded link generator 1015 can be configured to assign each of theencoded links generated by the encoded link generator 1015 that arelinked to the same resource to a respective media channel. In someembodiments, the encoded link generator 1015 can assign an encoded linkto a media channel by creating an entry in a data structure stored onthe database 230. In some embodiments, the entry can include theresource URL 1045 of the resource 1040, the first encoded media channellink 1055 a and the media channel ID of the first media channel 1050 a.In this way, any activity corresponding to the first encoded mediachannel link 1055 a can be attributed to the first media channel 1050 a.Similarly, the entry can include the resource URL 1045 of the resource1040, the second encoded media channel link 1055 b and the media channelID of the second media channel 1050 b. In this way, any activitycorresponding to the second encoded media channel link 1055 b can beattributed to the second media channel 1050 b.

The media channel analysis system 1000 may receive multiple requests togenerate media specific encoded links for the same resource frommultiple client devices or accounts corresponding to the media channelanalysis system. For instance, the media channel analysis system 1000can receive a first request from a first client device to generatemultiple encoded links for a particular resource. The media channelanalysis system 1000 can also receive a second request from a secondclient device to generate multiple encoded links for the same resource.In some embodiments, the media channel analysis system, via the encodedlink generator 1015 can generate a first set of encoded links for thefirst request and a second set of encoded links that are different fromthe first set of encoded links for the second request. In this way, anyactivity corresponding to any particular encoded link can be specific toa media channel and a client device from which the request to generateencoded links was received. However, in some embodiments, the mediachannel analysis system 1000, via the encoded link generator 1015, canprovide previously generated encoded links that are linked to the sameresource and are assigned to a particular media channel in response to arequest from another client device for an encoded link to the sameresource to be assigned to the same media channel. In such embodiments,any activity corresponding to the particular encoded link will bespecific to the media channel but will not be specific to a particularclient device.

In some embodiments, the media channel analysis system 1000 can beconfigured to maintain a data structure in the database 230 to allow themedia channel analysis system to identify, for a given resource, each ofthe encoded links to the resource generated by the encoded linkgenerator 1015. In addition, the media channel analysis system 1000 canbe further configured to identify, for a given resource, each of thegenerated encoded links to the resource that are assigned to aparticular media channel, such as FACEBOOK, or email or SMS, amongothers.

In some embodiments, multiple entries corresponding to requests togenerate encoded links to a resource can be linked together based on theresource or the URL of the resource. In some embodiments, the mediachannel analysis system 1000 can be configured to perform a lookup byresource URL or resource to identify each of the generated encoded linksto the resource. In this way, the media channel analysis system 1000 canidentify each of the encoded links generated by the encoded linkgenerator 1015 for the given resource.

The traffic analyzer 1025 of the media channel analysis system 1000 canbe configured to identify link activity 1010. Link activity can includeany actions performed by users of one or more client devices on encodedlinks generated by the encoded link generator 1015. Examples of actionscan include clicking on encoded links, sharing encoded links, requestingto generate the encoded links, among others. The media channel analysissystem 1000 can identify these actions and record the actions in thedatabase 230. In some embodiments, the database 230 can include one ormore data structures for recording link activity. For instance, eachtime a client device requests access (for instance, by clicking) to alink generated by the link generator 1015, the traffic analyzer 1025 canidentify the activity (for instance, the request to access the link) andupdate one of the data structures to include the identified activity. Insome embodiments, the traffic analyzer 1025 can identify clickinformation and store the click information in the data structure. Theclick information can include one or more of the following: a clientdevice identifier, a cookie of the media channel analysis system (orlinking system 120) assigned to the client device (or user), a sourceURL on which the encoded link was presented, date and time of theactivity, URL of the encoded link, a media channel to which the encodedlink is assigned, among others.

In some implementations, the traffic analyzer 1025 can generate entriescorresponding to actions taken on encoded links generated by the encodedlink generator 1015. In some implementations, the traffic analyzer 1025can maintain a table including a plurality of entries. Each entry caninclude one or more of an encoded link URL, a media channel to which theencoded link is assigned, a source identifier identifying the content onwhich the encoded link URL was presented when accessed, a cookie of thelinking system identifying a client device that accessed the encodedlink URL, a destination resource identifier identifying the resource towhich the encoded link is linked, among others.

Table 1 below illustrates example entries that can be generated by thetraffic analyzer 1025.

Media Client Encoded channel Channel Source device Destination Eventlink URL identifier Type Identifier identifier Resource URL Time Link 1Facebook Social Fb.com/123 Cookie1 Example1.com Dec. 1, 2015 Media1:02:03 Link 2 SMS SMS null Cookie2 Example1.com Dec. 1, 2015 2:52:03Link 3 Email Email null Cookie3 Example1.com Dec. 1, 2015 4:13:33 Link 2Facebook SMS Fb.com/123 Cookie4 Example1.com Dec. 1, 2015 1:02:03 Link 3Facebook Email Fb.com/524 Cookie5 Example1.com Dec. 1, 2015 2:52:03

As shown in Table 1, each of the links, Link 1, Link 2 and Link 3 eachare linked to the same destination resource, Examplel.com. Even thoughthe links were originally distributed on the media channel for whichthey were configured, the links may be shared across different mediachannel types. For instance, both Link 2 and Link 3, were shared onFacebook (social media), even though they were assigned to the SMSchannel and email channel respectively. By maintaining a table includinga plurality of entries for each action taken on an encoded linkgenerated by the encoded link generator 1015, the traffic analyzer 1025can use the table to determine statistics for various encoded links andmedia channels. These statistics can then be shared or provided toadvertisers that may determine where to allocate their advertising spendbased on the performance of the media channels. By generating separatelinks for each media channel and then using the respective linksassigned to a media channel to distribute the links on their respectivechannels, an advertiser, via the traffic analyzer, can determine theperformance of each of the links and the media channels to which theyare assigned.

The traffic analyzer 1025 can be configured to determine statisticsbased on activity on each of the links generated by the encoded linkgenerator 1015. The traffic analyzer 1025 can determine statisticsrelated to a given resource, for each media channel, based on activityon each of the links generated by the encoded link generator 1015 thatare assigned to the media channel and linked to the resource. Forinstance, if a resource (for instance, webpage 1) is linked to 3different encoded links generated by the encoded link generator 1015that are each assigned to a first media channel and linked to webpage 1,the traffic analyzer 1025 can identify all of the actions performed onthe 3 different encoded links and based on the identified actions,generate a report including statistics for the first media channel forthe webpage 1 based on the 3 different encoded links generated by theencoded link generator 1015.

The traffic analyzer 1025 can be configured to determine statistics fromvarious perspectives. In one embodiment, the traffic analyzer 1025 canbe configured to determine statistics based only on encoded linksgenerated by the encoded link generator 1015 that are linked to aparticular resource. In one embodiment, the traffic analyzer 1025 can beconfigured to determine statistics based only on encoded links generatedby the encoded link generator 1015 that are linked to a particularresource and assigned to a particular media channel. In one embodiment,the traffic analyzer 1025 can be configured to determine statisticsbased only on encoded links generated by the encoded link generator 1015that may be linked to multiple resources but are assigned to aparticular media channel. In one embodiment, the traffic analyzer 1025can be configured to determine statistics based only on encoded linksgenerated by the encoded link generator 1015 that are linked to aparticular resource and are specific to a particular client device (ormarketer) that requested the generation of the encoded links. In someembodiments, the traffic analyzer 1025 can be configured to determinestatistics based on one or more of previous embodiments.

In some embodiments, the traffic analyzer 1025 may generate orcalculate, for each media channel, a number of requests to access aparticular resource that were received. For example, for the first mediachannel 1050 a and the resource 1040, the traffic analyzer 1025 candetermine a total number of requests to access the resource 1040 viaencoded links that were generated by the encoded link generator 1015that were assigned to the first media channel 1050 a. For thisdetermination, in one embodiment, the traffic analyzer 1025 may identifyall the encoded links generated by the encoded link generator 1015 thatare assigned to the first media channel 1050 a. Next, with theidentified encoded links, the traffic analyzer 1025 may calculate atotal number of requests to access the resource 1040 by adding thenumber of requests to access the resource 1040 of each of the identifiedencoded links specific to the first media channel 1050 a.

In some embodiments, the traffic analyzer 1025 may generate orcalculate, for each media channel, a number of different source URLsfrom which requests to access the resource 1040 were received. Forexample, for the first media channel 1050 a and the resource 1040, thetraffic analyzer 1025 may determine a number of different source URLsfrom which requests to access the resource 1040 via encoded links thatwere generated by the encoded link generator 1015 that were assigned tothe first media channel 1050 a. For this determination, in oneembodiment, the traffic analyzer 1025 may identify all the encoded linksgenerated by the encoded link generator 1015 that are assigned to thefirst media channel 1050 a. Next, with the identified encoded links, thetraffic analyzer 1025 may identify, from the data structure maintainingan activity log for each of the encoded links, each of the differentsource URLs from which requests to access the resource 1040 via one ofthe retrieved encoded links were received.

In other embodiments, the traffic analyzer 1025 may determine, for theresource 1040, a statistic (for example, number of clicks) for each ofthe media channels, and aggregate each of the identified statistics togenerate an aggregate statistic for the resource. For example, tocalculate the aggregated total number of requests to access a particularresource via links encoded by the encoded link generator 1015 andassigned to the media channels, the traffic analyzer 1025 may add thenumber of requests to access the resource 1040 via the encoded linksassigned to a respective media channel of the media channels.

In some embodiments, the portion of the statistics may relate to aspecific media channel, for instance, one of a social networkingchannel, an SMS channel and an email channel. In some embodiments, thetraffic analyzer 1025 may be configured to rank each of the mediachannels based on their respective statistics. For example, aftercalculating, for each media channel, the number of requests to accessthe resource 1040 that were received, the traffic analyzer 1025 may rankthe media channels based on their respective number of requests toaccess the resource 1040 that were received. In another example, aftercalculating, for each media channel, the number of different source URLsfrom which requests to access the resource 1040 were received, thetraffic analyzer 1025 may rank the media channels based on theirrespective number of different source URLs from which requests to accessthe resource 1040 were received.

In some embodiments, the traffic analyzer 1025 may provide a mediachannel statistics output 1060 including at least a portion of thestatistics. For example, the traffic analyzer 1025 may provide the mediachannel statistics output 1060 by generating a visual representation ofthe statistics (e.g., plain text or table format) on a web page. In thismanner, for example, a table format output may be provided representing,for each media channel, the number of requests to access the resource1040 and the number of different source URLs from which requests toaccess the resource 1040 were received, along with their respectiveranks. In some other embodiments, the traffic analyzer 1025 may generatea graphical representation of the statistics, e.g., bar graphs, piecharts, line graphs and histogram. In this manner, for example, a bargraph or pie chart or histogram output may be provided representing, foreach media channel, the number of requests to access the resource 1040or the number of different source URLs from which requests to access theresource 1040 were received.

In some embodiments, the traffic analyzer 1025 may provide the mediachannel statistics output 1060 by providing statistics relating toperformance of an SMS channel or an email channel. In one embodiment,performance of an SMS channel or an email channel may relate toperformance in advertising a particular resource to the public via theSMS or email channels. Such advertisement performance may be measured bythe number of requests to access the particular resource or the numberof different source URLs from which requests to access the particularresource were received. For example, one performance metric of an SMSchannel may be its rank among multiple media channels or its relativevolume compared to those of other media channels with respect to thenumber of requests to access the resource 1040 or the number ofdifferent source URLs from which requests to access the resource 1040were received.

In some embodiments, the media channels may include a first channelcorresponding to a first domain and a second channel corresponding to asecond domain. In some embodiments, a media channels may relate tomultiple domains. Multiple domains may identify distinct networkresources, such as computers, networks or services on the Internet. Insome other embodiments, the media channels may correspond to differentgeographical regions. For instance, an advertiser may request togenerate separate encoded links for each State in the U.S., or differentencoded links for different countries.

In some other embodiment, the media channels may further include a firstchannel corresponding to a first advertisement of an advertiser and asecond channel corresponding to a second advertisement of theadvertiser. For example, the same business or company may utilizemultiple media channels for different advertisements.

Referring now to FIG. 10B, an embodiment of a method for analyzingtraffic across a plurality of media channels is depicted. In briefoverview, at step 1050, the system or server 1000 may receiveidentification of the media channels to distribute a link to a resource.At step 1055, the server 1000 may generate different encoded linkslinked to the resource for the identified media channels. At step 1060,the server 1000 may assign a respective encoded link of the generatedencoded links to each of the identified media channels. At step 1065,the server 1000 may determine, for each of the identified mediachannels, statistics related to the respective encoded link. At step1070, the server 1000 may provide an output comprising at least aportion of the statistics.

More particularly, at step 1050, the server 1000 may receiveidentification of the media channels to distribute a link to a resource.The server 1000 may receive identification of multiple media channelsincluding a first media channel 1050 a and a second media channel 1050 bto distribute a link to a resource 1040. The resource 1040 may have aresource URL 1045. In some embodiments, a client device may provide arequest for generating an encoded link that is linked to a resource, fora media channel. In some embodiments, the client device may provide arequest for generating an encoded link via web interface of the server1000. In some embodiments, the client device may provide a request forgenerating an encoded link via the linking system API or applications225 in the client device (see FIG. 2). The request for generating anencoded link may include identification of the media channel. That is,the server 1000 may receive identification of a media channel from aclient device via web interface of the server 1000 or the linking systemAPI or applications 225 in the client device (see FIG. 2). Theidentification of a media channel may include a URL of the media channel(e.g., “www.facebook.com” for the Facebook social media channel),application information of the media channel (e.g., the name of emailclient application for the email channel), or an identification numberof the media channel (e.g., telephone number of a mobile phone for theSMS channel).

At step 1055, the server 1000 may generate different encoded linkslinked to the resource for the identified media channels. In someembodiments, the server 1000 may generate, via the encoded linkgenerator 1015, a first encoded media channel link 1055 a and a secondencoded media channel link 1055 b that are encoded by the server of thelinking system 120 for the identified first media channel 1050 a andsecond media channel 1050 b, respectively. The first encoded mediachannel link 1055 a and second encoded media channel link 1055 b may belinked to the resource 1040. In some embodiments, responsive to arequest for generating an encoded link for a media channel, the encodedlink generator 1015 may retrieve the media channel IDs of the first andsecond media channels 1050 a and 1050 b. Next, the encoded linkgenerator 1015 may generate the first and second encoded media channellinks 1055 a and 1055 b via the encoded link generator 1015. The encodedlink generator 1015 may then store in a data structure, the resource URL1045 as original URL, the first encoded media channel link as encodedURL, and the media channel ID of the first media channel 1050 a as mediachannel ID. Similarly, the encoded link generator 1015 may store in theencoding data structure, the resource URL 1045 as original URL, thesecond encoded media channel link as encoded URL, and the media channelID of the second media channel 1050 b as media channel ID. In thismanner, the server 1000 can assign a respective encoded link of thegenerated first and second encoded media channel links 1055 a and 1055 bto each of the identified first and second media channels 1050 a and1050 b.

At step 1060, the server 1000 may assign a respective encoded link ofthe generated encoded links to each of the identified media channels. Insome embodiments, the server 1000 may assign a respective encoded linkof the generated first and second encoded media channel links 1055 a and1055 b to each of the identified first and second media channels 1050 aand 1050 b. In some embodiments, the server 1000 may assign therespective encoded link to each of the identified media channels byassigning the first encoded link 1055 a to a social networking channel,the second encoded link 1055 b to an SMS channel, and a third encodedlink (not shown) to an email channel. For this assignment, the mediachannel data structure of the database 230 may store data describing asocial networking channel, an SMS channel, and an email channel. Forexample, the Facebook social networking channel may be described in themedia channel data structure as “10” as media channel ID, “social media”as channel category, “Facebook” as channel name, and “facebook.com” aschannel domain. An email channel using the Google Gmail service may bedescribed in the media channel data structure as “11” as media channelID, “email” as channel category, “Gmail” as channel name, and“gmail.com” as channel domain. An SMS channel using a mobile phone maybe described in the media channel data structure as “12” as mediachannel ID, “SMS” as channel category, and the telephone number of themobile phone as channel name. In some embodiments, the SMS channel oremail channel may have a source address that is not a URL. In this case,the channel domain field of such SMS channel or email channel may be setto a “Null” value in the media channel data structure.

In some embodiments, the media channels may include a first channelcorresponding to a first domain and a second channel corresponding to asecond domain. In some embodiments, the media channels may relate torespective multiple domains. Multiple domains may identify distinctnetwork resources, such as computers, networks or services on theInternet. In some other embodiment, multiple domains may identifydifferent administrative realms such as different countries, geographiclocations, businesses or organizations. These various channel domainsmay be described using the channel domain field of the media channeldata structure. In some other embodiment, the media channels may furtherinclude a first channel corresponding to a first advertisement of anadvertiser and a second channel corresponding to a second advertisementof the advertiser. For example, the same business or company may utilizemultiple media channels for different advertisements. These multiplemedia channels for the same business or company may be represented bysetting, in the media channel data structure in the database 2300, thechannel name field to different advertisements while setting the channeldomain field to the same business or company.

At step 1065, the server 1000 may determine, via the traffic analyzer1025 for each of the identified media channels 1050 a and 1050 b,statistics related to the respective encoded link. In some embodiments,the traffic analyzer 1025 may generate or calculate, for each mediachannel, a number of requests to access a particular resource that werereceived. For example, given the information on the first media channel1050 a and the resource 1040, the traffic analyzer 1025 may calculatethe number of requests to access the resource 1040 via encoded linksthat was generated for the first media channel 1050 a. For thiscalculation, in one embodiment, the traffic analyzer 1025 may retrieveall the encoded links created for the first media channel 1050 a fromthe encoding data structure in the database 230. Next, with theretrieved encoded links, the traffic analyzer 1025 may calculate, fromthe user action data structure, the sum of the number of requests toaccess the resource 1040 via each of the retrieved encoded links tocalculate the total number of requests to access the resource 1040 viaany of the retrieved encoded links.

In some embodiments, the traffic analyzer 1025 may generate orcalculate, for each media channel, a number of different source URLsfrom which requests to access the resource 1040 were received. Forexample, given the information on the first media channel 1050 a and theresource 1040, the traffic analyzer 1025 may calculate the number ofdifferent source URLs from which requests to access the resource 1040via encoded links for the first media channel 1050 a were received. Forthis calculation, in one embodiment, the traffic analyzer 1025 mayretrieve all the encoded links generated for the first media channel1050 a from the encoding data structure in the database 230. Next, withthe retrieved encoded links, the traffic analyzer 1025 may count, fromthe source resource/channel URL field of the user action data structure,the number of different source URLs from which requests to access theresource 1040 via one of the retrieved encoded links were received.

In other embodiment, the server 1000 may identify, via the trafficanalyzer 1025 for the resource 1040, a first statistic for each of themedia channels, and aggregate each of the identified first statistics togenerate an aggregate first statistic for the resource. For example, tocalculate the aggregated total number of requests to access a particularresource via links encoded for a set of media channels, the trafficanalyzer 1025 may sum or aggregate the total number of requests toaccess the resource 1040 via links encoded for each of the set of mediachannels. Similarly, to calculate the aggregated number of differentsource URLs from which requests to access the resource 1040 via linksencoded for a set of media channels, the traffic analyzer 1025 may addthe number of different source URLs from which requests to access theresource 1040 via links encoded for each of the set of media channelswere received.

In some embodiments, the portion of the statistics may relate to aspecific media channel that is any one of social networking channel, SMSchannel and email channel. The media channel data structure in thedatabase 230 according to an embodiment may store information relatingto one of social networking channel, SMS channel and email channel. Forexample, the Facebook social networking channel may be described in themedia channel data structure as “10” as media channel ID, “social media”as channel category, “Facebook” as channel name, and “facebook.com” aschannel domain. An email channel using the Google Gmail service may bedescribed in the media channel data structure as “11” as media channelID, “email” as channel category, “Gmail” as channel name, and“gmail.com” as channel domain. An SMS channel using a mobile phone maybe described in the media channel data structure as “12” as mediachannel ID, “SMS” as channel category, and the telephone number of themobile phone as channel name. Therefore, using data stored in the mediachannel data structure, the encoded link generator 1015 may generate anencoded link for any one of social networking channel, SMS channel andemail channel and store the encoded link in the encoding data structure.Moreover, using data stored in the media channel data structure andencoding data structure, the media channel identifier 1020 may identifyuser actions relating to encoded links encoded for any one of socialnetworking channel, SMS channel, email channel, and store theinformation relating to the identified user actions in the user actiondata structure. Similarly, using data stored in the media channel datastructure and encoding data structure, the media channel identifier 1020may identify global actions relating to encoded links encoded for anyone of social networking channel, SMS channel and email channel andstore the information relating to the identified global actions in theglobal action data structure. Therefore, using data stored in the fourdata structures relating to encoded links encoded for any one of socialnetworking channel, SMS channel and email channel, the traffic analyzer1025 may generate channel-specific or aggregate statistics, a portion ofwhich relates to a specific media channel that is any one of socialnetworking channel, SMS channel and email channel.

In some embodiments, the server 1000 may rank the media channels basedon their respective statistics. For example, after calculating, for eachmedia channel, the number of requests to access the resource 1040 thatwere received, the traffic analyzer 1025 may rank the media channelsbased on their respective number of requests to access the resource 1040that were received. For another example, after calculating, for eachmedia channel, the number of different source URLs from which requeststo access the resource 1040 were received, the traffic analyzer 1025 mayrank each of the media channels based on their respective number ofdifferent source URLs from which requests to access the resource 1040were received, the traffic analyzer 1025.

In some embodiments, referring to FIG. 10A, the server 1000 may identifyvia the media channel identifier 1020, a common source URL acrossmultiple media channels. In some embodiments, for a particular resource,the media channel identifier 1020 may identify whether there exists acommon source URL across all or a subset of the media channels fromwhich requests to access the particular resource were received. In someother embodiments, the media channel identifier 1020 may also identifywhether there exists a common source URL across all or a subset of themedia channels from which requests to access any resource via encodedlinks were received. The identified common source URL can be effectivelyused by a user, e.g., advertiser, by increasing the sharing of links onthe identified common source URL.

At step 1070, the server 1000 may provide a media channel statisticsoutput 1060 including at least a portion of the statistics. In someembodiments, the server 1000 may provide, via the traffic analyzer 1025,a media channel statistics output 1060 including at least a portion ofthe statistics. For example, the traffic analyzer 1025 may provide themedia channel statistics output 1060 by generating text representationof the statistics (e.g., plain text or table format) on a web page. Inthis manner, for example, a table format output may be providedrepresenting, for each media channel, the number of requests to accessthe resource 1040 and the number of different source URLs from whichrequests to access the resource 1040 were received, along with theirrespective ranks. In some other embodiment, the traffic analyzer 1025may generate graphical representation of the statistics, e.g., bargraphs, pie charts, line graphs and histogram. In this manner, forexample, a bar graph or pie chart or histogram output may be providedrepresenting, for each media channel, the number of requests to accessthe resource 1040 or the number of different source URLs from whichrequests to access the resource 1040 were received.

In some embodiments, the server 1000 may provide, via the trafficanalyzer 1025, a media channel statistics output 1060 including at leasta portion of the statistics. For example, the traffic analyzer 1025 mayprovide the media channel statistics output 1060 by generating textrepresentation of the statistics (e.g., plain text or table format) on aweb page. In this manner, for example, a table format output may beprovided representing, for each media channel, the number of requests toaccess the resource 1040 and the number of different source URLs fromwhich requests to access the resource 1040 were received, along withtheir respective ranks. In some other embodiment, the traffic analyzer1025 may generate graphical representation of the statistics, e.g., bargraphs, pie charts, line graphs and histogram. In this manner, forexample, a bar graph or pie chart or histogram output may be providedrepresenting, for each media channel, the number of requests to accessthe resource 1040 or the number of different source URLs from whichrequests to access the resource 1040 were received.

In some embodiments, the server 1000 may provide the media channelstatistics output 1060 by providing statistics relating to performanceof an SMS channel or an email channel. In one embodiment, performance ofan SMS channel or an email channel may relate to performance inadvertising a particular resource to the public via the SMS or emailchannels. Such advertisement performance may be measured by the numberof requests to access the particular resource or the number of differentsource URLs from which requests to access the particular resource werereceived. For example, one performance metric of an SMS channel may beits rank among multiple media channels or its relative volume comparedto those of other media channels with respect to the number of requeststo access the resource 1040 or the number of different source URLs fromwhich requests to access the resource 1040 were received. The top-rankedmedia channels can be effectively used for a user, e.g., advertiser, byincreasing the sharing or forwarding of links across the top-rankedmedia channels.

K. Systems and Methods for Analyzing Online Content Audience Via EncodedLinks

Typically, a website can only track activities of visitors based onactions the visitors perform on the website but are unable to track theactivities of visitors on other websites. The present solution can trackthe activities of visitors across multiple websites that utilize encodedlinks encoded by a linking system, which can collect data about theactivities of visitors across the multiple websites that individualwebsites would otherwise be unable to gather. The present solution cangather intelligence about an audience of online content published byonline content publishers as well as provide metrics based on ananalysis of the collected activities of visitors across the multiplewebsites. Based on the collected data, the present solution can identifycorrelations between different websites/topics and content. Thisinformation may be useful to websites or online content publishers tounderstand the types of content their web site visitors are interestedin at an aggregate level and at an individual level.

The present solution may include a content audience analysis system thatcan deploy a cookie when a user of a client device clicks on a linkencoded by the content audience analysis system and track or monitor theuser's online activity as the user interacts (e.g., clicks) on otherencoded by the content audience analysis system on different resources(e.g., source URLs) corresponding to different websites or domains. Thecontent audience analysis system can aggregate, for a cookie, allinteractions (e.g., clicks) on links encoded by the linking system 120(described in FIGS. 2A-10B) performed by the user. The content audienceanalysis system can further aggregate, for a plurality of cookies, allor portion of interactions (e.g., clicks) on links encoded by thecontent audience analysis system performed by the users corresponding tothe cookies. With this configuration, the content audience analysissystem may determine, via cookies assigned to client devices of users,for each of the online content publishers for which the content audienceanalysis system has encoded at least one link to a resource of theonline content publisher, other resources, websites, visited by usersthat interacted with the at least one link.

Further, the content audience analysis system can analyze content ateach of the resources on which the encoded links are displayed as wellas the resources to which the encoded links are linked. The contentaudience analysis system can classify the resources to one or morecontent types and based on the classification, provide online contentpublishers additional information about the types of content theirvisitors are accessing. This can allow online content publishers tobetter understand their audiences and thereby, upload content on theirwebsite that caters to their audiences or to audiences they wish tobring to their website.

Referring to FIG. 11A, an embodiment of a content audience analysissystem 1100 for analyzing online content audience via encoded links isdepicted. In brief overview, the content audience analysis system 1100can be a part of the linking system 120 or may include components andfunctionality of the linking system 120, as shown in FIGS. 2A-10B. Thecontent audience analysis system 1100 may include a link generationengine 1115, a cookie management engine 1120, a content publisheridentification engine 125 and a data analyzer 1130. The content audienceanalysis system 1100 can be a part of a linking system, such as thelinking system 120 described herein with respect to FIGS. 2A-11B.

The link generation engine 1115 can include one or more executableinstructions, code, scripts, programs, or modules configured to receiverequests to generate encoded links. In some implementations, the linkgeneration engine 1115 can receive a request from a client device togenerate an encoded link. The request can include a resource identifierthat is associated with a resource or content for which to generate thelink. For instance, a content publisher, via a client device of thecontent publisher, can transmit a request to the link generation engine1115 to generate a link to a particular resource. The particularresource can be a resource hosted on a server of the content publisher(or accessible via a domain of the content publisher), or in someimplementations, a resource hosted on a server of any other contentpublisher or accessible via any other domain. The link generation engine1115 can generate the encoded link. The encoded link corresponds to aresource maintained by the content audience analysis system 1100 andconfigured such that when a request to access the encoded link isreceived by the content audience analysis system 1100, the contentaudience analysis system 1100 can transmit a response to the clientdevice requesting access redirecting the client device to the resourceto which the encoded link is linked. The content audience analysissystem 1100 is configured to store data associated with the request andcan use the information associated with the request to track onlineactivity, as will be described herein. In some implementations, the linkgeneration engine 1115 can include one or more components of otherengines, modules, or components described with respect to the linkingsystem 120 described with respect to FIGS. 2A-10B.

The link generation engine 1115 or the content audience analysis system1100 can maintain a plurality of encoded links and resources associatedwith the encoded links that are configured to cause client devicesrequesting to access the encoded links to be redirected to otherresources to which the encoded links are linked. In someimplementations, the content audience analysis system 1100 can maintaina database, such as database, that can be configured to maintain amapping of encoded links to resources to which to redirect clientdevices accessing the encoded links.

The cookie management engine 1120 can include one or more executableinstructions, code, scripts, programs, or modules configured to managecookies of the content audience analysis system 1100 as well as datacorresponding to the cookies. In some implementations, the cookiemanagement engine 1120 can be configured to assign cookies to clientdevices responsive to receiving a request to access an encoded linkgenerated by the link generation engine 1115. In some implementations,the cookie management engine 1120 can be configured to determine,responsive to the content audience analysis system 1100 receiving arequest to access an encoded link, if the request includes a cookie ofthe content audience analysis system 1100. In some implementations, thecookie management engine 1120 can assign the client device a cookie ofthe content audience analysis system 1100 responsive to determining thatthe request does not include a cookie of the content audience analysissystem 1100.

The cookie management engine 1120 can generate a cookie and assigns thegenerated cookie to the client device. This cookie can be included inthe response to the request and can be stored on the client device towhich the cookie is assigned. In some implementations, the cookie can bespecific to a browser of the client device from which the request toaccess the encoded link was received. Once a cookie has been assigned toa client device and transmitted to the client device, the client devicecan be configured to include the cookie in future requests to access anyencoded link generated by the content audience analysis system 1100.

The cookie management engine 1120 can be configured to receive aplurality of interactions with encoded links generated by the contentaudience analysis system 1100 and linked to resources of one or morecontent publishers. In some implementations, each time users of clientdevices access an encoded link generated by the content audienceanalysis system 1100, the cookie management engine 1120 can identify acookie associated with the client device from the request.

The cookie management engine 1120 can be configured to store andmaintain activity data corresponding to requests associated with aparticular cookie in a data log corresponding to the cookie. Theactivity data can include i) an identification of the resource on whichthe encoded link was presented when accessed (source resource URL); ii)an identification of the resource to which the encoded link is linked(destination resource URL); iii) time/date of access; iv) identity ofcontent publisher of source resource URL; v) identity of contentpublisher of destination resource URL; vi) type/topic of contentincluded in source resource; vii) type/topic of content included indestination resource, among others. In some implementations, a resourceanalysis module (not shown) can be configured to analyze each of thesource resource and the destination resource to determine a type ofcontent included in the source resource and the destination resource. Insome implementations, the resource analysis module (not shown) can beconfigured to analyze each of the source resource and the destinationresource to determine a topic of the content included in the sourceresource and the destination resource. Examples of topics can includesports, music, news, entertainment, shopping, electronics, etc., and insome implementations, the granularity of the topics may be morespecific, for example, tennis, rap music, political news, among others.

The cookie management engine 1120 can be configured to receive aplurality of interactions with encoded links generated by the contentaudience analysis system 1100 and linked to resources of a first contentpublisher. In some implementations, the cookie management engine 1120can receive a request from a first content publisher to provide datacorresponding to visitors accessing resources of the first contentpublisher via encoded links generated by the link generation engine 1115of the content audience analysis system 1100. In some implementations,the cookie management engine 1120 can generate, for the first contentpublisher, data corresponding to visitors accessing resources of thefirst content publisher via encoded links generated by the linkgeneration engine 1115 of the content audience analysis system 1100.

In some implementations, the cookie management engine 1115 can beconfigured to identify a plurality of encoded links generated by thecontent audience analysis system 1100 that are linked to resources of afirst content publisher. The cookie management engine 1115 can identifythe plurality of encoded links by performing a lookup in the database230 to identify each of the encoded URLs that are linked to resources ofthe first content publisher. For instance, if the first contentpublisher is www.examplel.com, then the cookie management engine 1115can identify the resources that are associated with the websitewww.examplel.com by performing a lookup for all resources that maycorrespond to the domain examplel.com.

In some implementations, the cookie management engine 1115 can beconfigured to identify a plurality of interactions with each of theplurality of encoded links generated by the content audience analysissystem 1100 that are linked to resources of the first content publisher.These interactions can correspond to clicks or other types of actionsthat result in the content audience analysis system 1100 receiving arequest to access an encoded link of the plurality of encoded links. Insome implementations, the cookie management engine 1115 can beconfigured to identify the plurality of interactions with each of theplurality of encoded links by performing a lookup in one or more datastructures stored in the database 230 using the encoded links. Forinstance, the cookie management engine 1115 can access a data structurethat stores entries corresponding to interactions to access encodedlinks. The cookie management engine 1115 can perform a lookup for allinteractions that correspond to encoded links generated by the contentaudience analysis system 1100 that are linked to resources of a firstcontent publisher.

The cookie management engine 1115 can be configured to identify, fromthe plurality of interactions, a plurality of cookies of the contentaudience analysis system 1100 assigned to unique client devices. In someimplementations, the cookie management engine 1115 can be configured toidentify, for each interaction of the plurality of interactions, acookie of a client device from which the request to access an encodedlink generated by the content audience analysis system 1100 that islinked to a resource of the first content publisher was received.

The content publisher identification engine can include one or moreexecutable instructions, code, scripts, programs, or modules configuredto identify one or more content publishers that include at least oneresource that is linked to an encoded link generated by the linkgeneration engine 1115 and that has been accessed via the encoded linkby one of the cookies identified by the cookie management engine 1115.In particular, the content publisher identification engine can beconfigured to identify second content publishers having resources thatwere accessed by the client devices corresponding to cookies identifiedby the cookie management engine 1115 via encoded links generated by thelink generation engine 1115.

After the cookie management engine 1115 identifies a cookie that isassociated with a client device that has requested to access a linkencoded by the link generation engine 1115 that is linked to a resourceof the first content publisher, the content publisher identifier engine1125 can identify one or more second content publishers that haveresources that have been accessed by the client device corresponding tothe cookie. To do so, in some implementations, the content publisheridentifier engine 1125 can access a data structure that includes one ormore entries corresponding to requests to access content via one or moreencoded links generated by the link generation engine 1115. The contentpublisher identifier engine 1125 can perform a lookup in the datastructure for entries corresponding to the identified cookie associatedwith the client device that has requested to access a link encoded bythe link generation engine 1115 that is linked to a resource of thefirst content publisher. Responsive to identifying the entries of theidentified cookie, the content publisher identifier engine 1125 can beconfigured to identify, from the identified entries, one or more contentpublishers different from the first content publisher whose resourceshave been accessed by the client device via encoded links generated bythe link generation engine 1115.

The content publisher identifier engine 1125 can be configured toaggregate, for each cookie that visited a resource of the first contentpublisher via a link generated by the link generation engine 1115, alist of content publishers having resources also visited by the cookievia one or more links generated by the link generation engine 1115. Insome implementations, the content publisher identifier engine 1125 canbe configured to determine, for each content publisher of the list ofcontent publishers, a type of content published by the content publisheror a topic of the content included in the resource of the contentpublisher accessed by the cookie. The content publisher identifierengine 1125 can be configured to store, in a data structure, for eachcookie associated with a client device, information relating to thecontent publishers visited by the client device, information related totopics of content accessed by the client device, among others.

The content publisher identifier engine 1125 can be configured toaggregate, for the first content publisher, using the techniquesdeployed herein to determine information about a particular cookie,information pertaining to activities of a plurality of cookies thataccessed a resource of the first content publisher via a link generatedby the link generation engine 1115.

The content publisher identifier engine 1125 can be configured to thenprovide, to the first content publisher, data corresponding to theidentified second content publishers having resources accessed by clientdevices that also accessed the resources of the first content publisher.

Referring to FIGS. 11A, 11B and 11C, the cookie management engine 1120may be further configured to store the plurality of requests to accessencoded links in a data structure (e.g., a resource requests table 1160in the database 230). In some embodiments, the data structure includesdata identifying a cookie provided to a client device (e.g., a field ofcookie ID 1161), a source URL (e.g., a data field of source resource1162) of a source resource on which the encoded link was presented, theidentity of the content publisher of the source resource (e.g., a datafield of source content publisher ID 1163), the destination URL of adestination resource accessed via the encoded link (e.g., a data fieldof destination resource 1164), the identity of the content publisher ofthe destination resource (e.g., a data field of destination contentpublisher ID 1165. In some embodiments, the data structure may includedata identifying the timestamp of each of the requests (e.g., a field oftimestamp 1166).

Referring to FIG. 11B, the database 230 may include any information ordata associated with clicks or other activity on one or more encodedlinks generated by the link generation engine 1115. The database 230 mayinclude data collected, tracked, and analyzed either statically or inreal-time, and associated with the click. In some embodiments, thedatabase 230 may include multiple data structures, e.g., multiplerelational database tables. For example, the database 230 may include aresource requests data structure including data related to requests fromclient devices to access a resource via encoded links generated by thelink generation engine 1115. In some embodiments, the resource requestdata structure may be implemented as the resource requests table 1160 asshown in FIG. 11B. The resource requests table 1160 may store one ormore entries, each of which corresponds to a request from a clientdevice for an online resource. The resource requests table 1160 may bedefined as multiple attributes or fields, for example, the cookie ID1161, the source resource field 1162, the source content publisher IDfield 1163, the destination resource 1164, the destination contentpublisher ID field 1165, and the timestamp 1166. The cookie ID field1161 may identify a cookie associated with the client device from whicheach request is received. In some embodiments, the database 230 mayinclude a cookie data structure (e.g., a cookie table) including datarelated to each cookie so that the cookie ID field 1161 can beassociated with an ID field of the cookie table. The source resourcefield 1162 may identify a source resource (or its URL) on which theencoded link was presented. The source content publisher ID field 1163may identify a content publisher (e.g., a person, company, organizationor any entity that can publish online resources) which published thesource resource identified by the source resource field 1162. Thedestination resource field 1164 may identify a destination resource (orits URL) to which the encoded link was linked. The destination contentpublisher ID field 1165 may identify a content publisher (e.g., aperson, company, organization or any entity that can publish onlineresources) which published the destination resource identified by thedestination resource field 1162. In some embodiments, the timestampfield 1166 may identify the date and time when each of the requests wasmade by a client device.

In some embodiments, referring to FIG. 11B, the database 230 may includea content publisher data structure (e.g., a content publisher table1167) including data associating a plurality of resources (e.g., a fieldof resource 1168) with their corresponding content publishers (e.g., afield of content publisher ID 1169) so as to identify a contentpublisher for a given resource, e.g., a source resource or a destinationresource in the resource requests table 1160. In some embodiments, thecontent publisher table 1167 may include data related to information oneach content publisher, for example, name of organization, geographicarea, industry, product, and contact information, etc. In someembodiments, the content publisher table 1167 may be populated with datafrom external sources, e.g., an internet domain name directory or searchservices. In some embodiments, referring to FIG. 11B, a single contentpublisher may have more than one resources. For example, the tworesources “dogs.com” and “cats.com” may be published by a singleorganization. In some embodiments, given a source resource and adestination resource, the corresponding source content publisher IDfield 1163 and destination content publisher ID field 1165 may bepopulated in the resource requests table 1160 using the data stored inthe content publisher table 1167.

Referring to FIG. 11A, the content audience analysis system 1100 maycomprise an application, program, library, process, service, script,task or any type and form of executable instructions executable orexecuting on a device. The content audience analysis system 1100 mayoperate on a plurality of servers 106A-106N. The content audienceanalysis system 1100 may include application programming interfaces,such as web services, XML, Jason (JSON), etc. for accessing thefunctionality, operations and/or data of the linking system. The contentaudience analysis system 1100 may include one or more modules,components or executables for providing these APIs and performing thefunction and operations described herein. For example, in someembodiments, the content audience analysis system 1100 may includecomponents like the link generation engine 1115, cookie managementengine 1120, content publisher identification engine 1125 and dataanalyzer 1130. Each of the components may comprise an application,program, library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. Themodules, components or executables of the content audience analysissystem may operate in a client/server architecture. The modules,components or executables of the content audience analysis system mayoperate in a distributed manner across multiple devices.

In some embodiments, an application (like the application 225 in FIG. 2)may execute on the client that communicates with or interfaces to thecontent audience analysis system to encode and decode URLs. In someembodiments, an application may include any portion of the contentaudience analysis system. In some embodiments, the application may be amobile application, generally referred to as an app, executing on amobile device, such as a smart phone or tablet device. In someembodiments, the application may include an add-on, extension, script,ActiveX control, applet, widget or other types and forms of executableinstructions executed by or in a browser. In some embodiments, theapplication may include, use or call one or more APIs to the linkingsystem. The application may be programmed to programmatically integratethe content audience analysis system, or interface thereto, into theapplication. Via the one or more APIs, the application may access datafrom the content audience analysis system. Via the one or more APIs, theapplication may perform or execute any of the functions or operations ofthe content audience analysis system. Via the one or more APIs, theapplication may perform or execute any of the systems and methodsdescribed herein.

Referring to FIGS. 11A and 11B, in some embodiments, the cookiemanagement engine 1120 receives from a first client device, a pluralityof interactions (e.g., clicks) on encoded links that are linked toresources of the first content publisher 1150 a and presented onrespective source resources. For example, referring to FIG. 11B, theplurality of interactions from the first client device may include afirst click on an encoded link linked to “mtv.com” that was presented on“yahoo.com”, a second click on the encoded link linked to “mtv.com” thatwas presented on “facebook.com”, and a third click on an encoded linklinked to “abc.com” that was presented on “facebook.com.” From theplurality of interactions, the cookie management engine 1120 mayidentify a cookie of the content audience analysis system 1100 assignedto the first client device. For example, the cookie management engine1120 may identify the cookie assigned to the first client device andretrieve the ID of the cookie (e.g., C1) by looking up the database 230with information of the first client device. Upon receiving of theplurality of interactions from the first client device, the cookiemanagement engine 1120 may also receive the access requestscorresponding to the interactions to access the encoded links. In someembodiments, the cookie management engine 1120 may store datacorresponding to the requests received from the first client device. Forexample, referring to FIG. 11B, the cookie management engine 1120 mayidentify the content publisher IDs of the resource “mtv.com” and twosource resources “yahoo.com” and “facebook.com” as P1, P3 and P4,respectively using the content publisher table 1167. The cookiemanagement engine may then store three database tuples having cookie ID“C1” corresponding to the three requests (and the first, second andthird clicks) in the resource requests table 1160 (see FIG. 11B).

Similarly, referring to FIGS. 11A and 11B, the cookie management engine1120 can receive from a second client device, a plurality ofinteractions (e.g., clicks) on encoded links that are linked toresources of the first content publisher 1150 a and presented onrespective source resources. For example, referring to FIG. 11B, theplurality of interactions from the second client device may include afourth click on an encoded link linked to “mtv.com” that was presentedon “dogs.com”, and a fifth click on the encoded link linked to “mtv.com”that was presented on “cats.com.” From the plurality of interactions,the cookie management engine 1120 may identify a cookie of the contentaudience analysis system 1100 assigned to the second client device andretrieve the ID of the cookie (e.g., C2) by looking up the database 230with information of the second client device. Upon receiving of theplurality of interactions from the second client device, the cookiemanagement engine 1120 may also receive the access requestscorresponding to the interactions to access the encoded links. In someembodiments, the cookie management engine may store data correspondingto the requests received from the second client device. For example,referring to FIG. 11B, the cookie management engine may identify thecontent publisher IDs of the resource “mtv.com” and two source resources“dogs.com” and “cats.com” as P1, P5 and P5, respectively using thecontent publisher table 1167. The cookie management engine may thenstore two database tuples having cookie ID “C2” corresponding to the tworequests (and the fourth and fifth clicks) in the resource requeststable 1160 (see FIG. 11B).

In some embodiments, the content publisher identification engine 1125may identify second content publishers having resources that wereaccessed by the same client devices corresponding to the plurality ofcookies. In some embodiments, the identification may be performed byissuing a database query to the database 230, e.g., SQL SELECTstatement, on the resource requests table 1160. For example, byselecting all tuples having the cookie ID “C1” from the resourcerequests table 1160, the identification engine 1125 may identify twocontent publishers, those with content publisher ID “P1” and “P2”,having the resources “mtv.com” and “abc.com” that were accessed by thesame first client device corresponding the cookie ID “C1”. In someembodiments, the content publisher identification engine 1125 mayprovide, to a first content publisher, data corresponding to identifiedcontent publishers having resources accessed by the same client devicesthat also accessed resources of the first content publisher. Forexample, by selecting all tuples having the cookie ID “C1” and nothaving the destination content publisher ID “P1”, the content publisheridentification engine 1125 may provide, to the first content publisherwith ID “P1”, data corresponding to the identified content publishers(e.g., the content publisher with ID “P2”) having resources (e.g.,“abc.com”) accessed by the same first client device that also accessedresources (e.g., “mtv.com”) of the content publisher with ID “P1”. Inaddition, since the content audience analysis system 1100 alsoidentifies the source resource on which the encoded link was presentedwhen the access request was received, the content publisheridentification engine 1125 can identify, for the first content publisherP1 (mtv.com), the second content publishers yahoo.com (P3), facebook.com(P4), and dogs.com (P5) as publishers having resources that were visitedby the first client device corresponding to the cookie ID C1. In someimplementations, the link generation engine 1115 may generate encodedlinks linked to resources of a particular content publisher so that theparticular content publisher can be identified from the encoded links.In some embodiments, the link generation engine 1115 may encode an URLinto an encoded URL that comprises a domain name configured, specifiedor identified by the particular content publisher. In some embodiments,the link generation engine 1115 may encode an URL into an encoded URLthat comprises a content publisher ID (see the content publisher table1167 in FIG. 11B) identifying the particular content publisher. Forexample, referring to FIG. 11B, the link generation engine 1115 mayencode an URL of the resource “dogs.com” into an encoded URL thatcomprises content publisher ID “5”. Similarly, the link generationengine 1115 may encode an URL of the resource “cats.com” into an encodedURL that comprises content publisher ID “5” because both “dogs.com” and“cats.com” belong to the same content publisher.

In some embodiment, referring to FIGS. 11A and 11B, the contentpublisher identification engine 1125 may receive, from the first contentpublisher 1150 a, a request to identify content publishers that haveresources that have been accessed by client devices that also accessedthe first content publisher 1150 a. In some embodiments, the firstcontent publisher 1150 a may use an application including a browser tosend a resource identification request to the content publisheridentification engine 1125. In some embodiments, the application may bea mobile application, generally referred to as an app, executing on amobile device, such as a smart phone or tablet device. In someembodiments, the application may include an add-on, extension, script,ActiveX control, applet, widget or other types and forms of executableinstructions executed by or in a browser. In some embodiments, theapplication may include, use or call one or more APIs to the contentaudience analysis system 1100. The application may be programmed toprogrammatically integrate the content audience analysis system 1100, orinterface thereto, into the application. Via the one or more APIs, theapplication may access data from the content audience analysis system1100. Via the one or more APIs, the application may perform or executeany of the functions or operations of the content audience analysissystem 1100.

Via the one or more APIs, the application may perform or execute any ofthe systems and methods described herein. In some embodiments,responsive to the resource identification request from a particularcontent publisher, the content publisher identification engine 1125 mayidentify content publishers that have resources that have been accessedby client devices that also accessed the particular content publisher.In some embodiments, for example, referring to FIG. 11B, the contentpublisher identification engine 1125 may identify content publishersthat have resources that have been accessed by client devices that alsoaccessed the (destination) content publisher with ID “P1”, by firstselecting data in the cookie ID field 1161 of all tuples having “P1” inthe destination content publisher field. Next, responsive toidentification of client devices with cookie IDs “P1” and “P2” as aresult of the selection, the content publisher identification engine1125 may further select data in the destination content publisher IDfield 1165 of all tuples having “C1” or “C2” in the cookie ID field.Next, as a selection result, the content publisher identification engine1125 may identify two destination content publishers with IDs “P1” and“P2”. Finally, the content publisher identification engine 1125 mayidentify the content publisher with ID “P2” as a content publisher(other than the content publisher with ID “P1”) that has resources thathave been accessed by the client devices that also accessed the(destination) content publisher with ID “P1”.

In some embodiments, referring to FIG. 11A, the content audienceanalysis system 1100 may receive a plurality of requests to accessdestination resources of a plurality of content publishers viainteractions with encoded links linked to the destination resources. Insome embodiments, the content audience analysis system 1100 may store,for each cookie of the plurality of cookies, in memory, data indicatingi) the destination resources of the plurality of content publishersaccessed by the client device of the cookie, ii) the source resources onwhich encoded links linked to the resources accessed by the clientdevice of the cookie; and iii) second resources accessed by the clientdevice of the cookie. For example, in some embodiments, referring toFIG. 11B, the server may store in memory (e.g., as a data report 1156),for the cookie with ID “C1”, data indicating i) the destinationresources of the plurality of content publishers accessed by the clientdevice of the cookie (e.g., “mtv.com”), ii) the source resources onwhich encoded links linked to the resources accessed by the clientdevice of the cookie (e.g., “yahoo.com” and “facebook.com”), and iii)second resources accessed by the client device of the cookie (e.g.,“abc.com”). Similarly, in some embodiments, referring to FIG. 11B, theserver may store in memory (e.g., as the data report 1156), for thecookie with ID “C2”, data indicating i) the destination resources of theplurality of content publishers accessed by the client device of thecookie (e.g., “mtv.com”), ii) the source resources on which encodedlinks linked to the resources accessed by the client device of thecookie (e.g., “dogs.com” and “cats.com”), and iii) second resourcesaccessed by the client device of the cookie (e.g., Null for the cookiewith ID “C2”).

In some embodiments, the server may provide to a content publisher ofthe plurality of content publishers (e.g., the content publisher with ID“P1”), statistics derived from the stored data. For example, forproviding to the content publisher with ID “P1”, the statistics mayinclude i) the number of unique source resources or correspondingcontent publishers on which encoded links linked to the destinationresources of the content publisher with ID “P1” were presented (e.g.,four unique source resources—“yahoo.com”, “facebook.com”, “dogs.com” and“cats.com”); ii) the most frequent source resource on which encodedlinks linked to the destination resources of the content publisher withID “P1” were presented; iii) the most frequent content publisher havingresources on which encoded links linked to the destination resources ofthe content publisher with ID “P1” were presented (e.g., the mostfrequent content publisher having source resources is the contentpublisher with ID “P5” having two source resources “dogs.com” and“cats.com”; and iv) the number of other content publishers that areaccessed by client devices that also accessed destination resources ofthe content publisher with ID “P1” (e.g., there is only one othercontent publisher (the one with ID “P2”) that is accessed by the sameclient device(s) that also accessed destination resources of the contentpublisher with ID “P1”).

In some embodiments, referring to FIG. 5A, the phrase list 522 andkeyword extractor 515 may be used to extract a topic based on thecontent included in a destination resource. Referring to FIG. 5A, thephrases list 522 may comprise any data and information identifying apredetermined set of phrases and/or keywords. The phrases list maycomprise a dictionary. The phrases list may comprise an ontology. Thephrases list may comprise an enumerated list of phrases and/or keywords.The phrases list may comprise an enumerated list of phrases and/orkeywords ranked in order of priority or otherwise having an identifiedpriority. The phrases list may comprise an enumerated list of phrasesand/or keywords ordered based on ranking or otherwise having anidentified ranking. The phrases list may comprise an enumerated list ofphrases and/or keywords with assigned weights or weighting. The phraseslist may identify a predetermined list of topics, interests or subjectmatter. The phrases list may identify a predetermined set of keywordsrelated to or making up a topics, interests or subject matter. Thephrases list may be generated from a third-party source, such as aweb-site or URL. The phrases list may be generated by the trendingengine based on a count of phrases and/or keywords identified in thepredetermined list of web-sites. The phrases list may be generated fromprevious versions of the phrases list. The phrases list may be generatedbased on learning or intelligence of the trending engine.

Referring to FIGS. 5A and 11A, the keyword extractor 515 may identifykeywords or topics from a predetermined set or list of destinationresources (e.g., the first resource 1140 a and the second resource 1140b). In some embodiments, the keywords or topics identified by thekeyword extractor 515 may be a subset of the phrases list. In someembodiments, the keyword extractor identifies keywords from apredetermined set or list of destination resources on a predeterminedfrequency. In some embodiments, the keyword extractor identifieskeywords from a predetermined set or list of destination resourcesresponsive to an event, such as a user request. In some embodiments, thekeyword extractor operates responsive to a click-stream whileidentifying keywords from a predetermined set or list of destinationresources.

In some embodiments, the server may determine, for a destinationresource of the destination resources, a topic based on the contentincluded in the destination resource using the phrases list 522 or thekeyword extractor 515. In some embodiments, the server may prepare thephrases list 522, and then determine for a destination resource, topicsor keywords based on the content of the destination resource byextracting the topics or keywords from the content of the destinationresource using the keyword extractor 515. In some embodiments, theserver may generate a list including one or more of the second resourcesthat also relate to each of the determined keywords or topics. Forexample, referring to FIG. 11B, the server may determine a topic orkeyword “music” from the content of the destination resource “mtv.com”using the keyword extractor 515. The server may also generate a listincluding the second (destination) resource “abc.com” that also relatesto the determined topic or keyword “music.”

In some embodiments, referring to FIGS. 11A and 11B, the system 1100 mayprovide, to a particular content publisher of the plurality of contentpublishers, a list including one or more domains corresponding to theresources accessed by client devices that also accessed resources of theparticular content publishers. For example, referring to FIG. 11B, theserver may provide, to the content publisher with ID “2”, a listincluding one or more domains (e.g., “mtv.com”) corresponding to theresource “mtv.com” accessed by the client device of the cookie ID “1”that also accessed the resource “abc.com” of the content publisher withID “2”. Similarly, referring to FIG. 11B, the server may provide, to thecontent publisher with ID “1”, a list including one or more domains(e.g., “mtv.com”) corresponding to the resource “abc.com” accessed bythe client device of the cookie ID “1” that also accessed the resource“mtv.com” of the content publisher with ID “1”.

In some embodiments, referring to FIGS. 11A and 11B, the system 1100 mayprovide, to a first content publisher of the plurality of contentpublishers, a number of cookies of the linking system that accessed afirst resource of the first content publisher and a second resource of asecond content publisher of the plurality of content publishers. Forexample, the server may provide, to the content publisher with ID “P1”,the number of distinct cookies of the linking system that accessed boththe resource “mtv.com” of the content publisher with ID “P1” and theresource “abc.com” of the content publisher with ID “P2” (e.g., thenumber of distinct cookies accessing both of the resources “mtv.com” and“abc.com” is 1—the cookie with ID “C1”).

Referring now to FIG. 11C, an embodiment of a method for analyzingonline content audience is depicted. In brief overview, at step 1180,the system or server 1100 may receive interactions with encoded linksgenerated by linking system and linked to resources of a first contentpublisher. At step 1182, the server may identify from the interactions,cookies assigned to unique client devices. At step 1184, the server mayIdentify second content publishers having resources that were accessedby client devices corresponding to the cookies via encoded linksgenerated by the server. At step 1186, the server may provide, to thefirst content publisher, data corresponding to the identified secondcontent publishers having resources accessed by client devices that alsoaccessed the resources of the first content publisher.

More particularly, referring to FIG. 11C, at step 1180, the system orserver 1100 may receive interactions with encoded links generated bylinking system and linked to resources of a first content publisher. Insome embodiments, referring to FIGS. 11A and 11B, the cookie managementengine 1120 receives from a first client device, a plurality ofinteractions (e.g., clicks) on encoded links that are linked toresources of the first content publisher 1150 a and presented onrespective source resources. For example, referring to FIG. 11B, theplurality of interactions from the first client device may include afirst click on an encoded link linked to “mtv.com” that was presented on“yahoo.com”, a second click on the encoded link linked to “mtv.com” thatwas presented on “facebook.com”, and a third click on an encoded linklinked to “abc.com” that was presented on “facebook.com.” From theplurality of interactions, the cookie management engine 1120 mayidentify a cookie of the linking system 120 assigned to the first clientdevice. For example, the cookie management engine 1120 may identify thecookie assigned to the first client device and retrieve the ID of thecookie (e.g., 1) by looking up the database 230 with information of thefirst client device, e.g., an IP address of the first client device.Upon receiving of the plurality of interactions from the first clientdevice, the cookie management engine 1120 may also receive the accessrequests corresponding to the interactions to access the encoded links.In some embodiments, the cookie management engine may store datacorresponding to the requests received from the first client device. Forexample, referring to FIG. 11B, the cookie management engine mayidentify the content publisher IDs of the resource “mtv.com” and twosource resources “yahoo.com” and “facebook.com” as 1, 3 and 4,respectively using the content publisher table 1167. The cookiemanagement engine may then store three database tuples having cookie ID“1” corresponding to the three requests (and the first, second and thirdclicks) in the resource requests table 1160 (see FIG. 11B).

Similarly, in some embodiments, referring to FIGS. 11A and 11B, thecookie management engine 1120 receives from a second client device, aplurality of interactions (e.g., clicks) on encoded links that arelinked to resources of the first content publisher 1150 a and presentedon respective source resources. For example, referring to FIG. 11B, theplurality of interactions from the second client device may include afourth click on an encoded link linked to “mtv.com” that was presentedon “dogs.com”, and a fifth click on the encoded link linked to “mtv.com”that was presented on “cats.com.”

In some embodiments, referring to FIGS. 11A and 11B, the link generationengine 1115 may generate encoded links linked to resources of aparticular content publisher so that the particular content publishercan be identified from the encoded links. In some embodiments, the linkgeneration engine 1115 may encode an URL into an encoded URL thatcomprises a domain name configured, specified or identified by theparticular content publisher. In some embodiments, the link generationengine 1115 may encode an URL into an encoded URL that comprises acontent publisher ID (see the content publisher table 1167 in FIG. 11B)identifying the particular content publisher. For example, referring toFIG. 11B, the link generation engine 1115 may encode an URL of theresource “dogs.com” into an encoded URL that comprises content publisherID “5”. Similarly, the link generation engine 1115 may encode an URL ofthe resource “cats.com” into an encoded URL that comprises contentpublisher ID “5” because both “dogs.com” and “cats.com” belong to thesame content publisher.

In some embodiments, referring to FIGS. 11A and 11B, the cookiemanagement engine 1120 may store the plurality of requests to accessencoded links in a data structure, the data structure including dataidentifying the source URL on which the encoded link was presented, theidentity of the first content publisher, the destination URL identifyingthe resource of the first content publisher, and the cookie provided tothe client device. For example, referring to FIG. 11B, the cookiemanagement engine 1120 may store the plurality of requests to accessencoded links in the resource requests table 1160, which includes dataidentifying a cookie provided to a client device (e.g., the cookie IDfield 1161), a source URL (e.g., the source resource field 1162) of asource resource on which the encoded link was presented, the identity ofthe content publisher of the source resource (e.g., the source contentpublisher ID field 1163), the destination URL of a destination resourceaccessed via the encoded link (e.g., the destination resource field1164), the identity of the content publisher of the destination resource(e.g., the destination content publisher ID field 1165).

Referring to FIG. 11C, at step 1182, the server may identify from theinteractions, cookies assigned to unique client devices. In someembodiments, referring to FIGS. 11A and 11B, from the plurality ofinteractions, the cookie management engine 1120 may identify a cookie ofthe linking system 120 assigned to the second client device and retrievethe ID of the cookie (e.g., 2) by looking up the database 230 withinformation of the second client device, e.g., an IP address of thesecond client device. Upon receiving of the plurality of interactionsfrom the second client device, the cookie management engine 1120 mayalso receive the access requests corresponding to the interactions toaccess the encoded links. In some embodiments, the cookie managementengine may store data corresponding to the requests received from thesecond client device. For example, referring to FIG. 11B, the cookiemanagement engine may identify the content publisher IDs of the resource“mtv.com” and two source resources “dogs.com” and “cats.com” as 1, 5 and5, respectively using the content publisher table 1167. The cookiemanagement engine may then store two database tuples having cookie ID“2” corresponding to the two requests (and the fourth and fifth clicks)in the resource requests table 1160 (see FIG. 11B).

In some embodiments, referring to FIG. 11A, when receiving the pluralityof interactions from a first client, the cookie management engine 1120may receive each request to access an encoded link from the first clientdevice. For example, responsive to an interaction taken on the firstencoded link 1152 a that is linked to the first resource 1145 a andincluded in the third resource 1154 a as source resource, the firstclient device may generate and transmit a request to the cookiemanagement engine 1120 to access the first resource 1140 a. The cookiemanagement engine 1120 may also identify i) a source URL identifying aresource on which the encoded link was presented and ii) a cookie of thelinking system that is unique to the client device. For example, thecookie management engine 1120 may receive the request for access to thefirst resource 1145 a and identify, responsive to receiving the request,from the request, i) the source URL identifying the third resource 1154a on which the encoded link 1152 a was presented and ii) a cookie of thelinking system that is unique to the first client device. In addition,the cookie management engine 1120 may generate a cookie responsive todetermining that the cookie management engine did not previously assigna cookie to the browser of the first client device from which therequest was received.

In some embodiments, referring to FIGS. 11A and 11B, the cookiemanagement engine 1120 may identify, for each request, a source contentpublisher corresponding to the resource identified by the source URL.For example, as noted above, the cookie management engine 1120 mayidentify from the request, the source URL identifying the third resource1154 a on which the encoded link 1152 a was presented. Next, in someembodiments, the cookie management engine 1120 may identify a sourcecontent publisher corresponding to the source URL of the third resource1154 by looking up the content publisher table 1167. For example, whenthe cookie management engine 1120 identifies from a request (e.g., therequest represented by the first tuple in the resource requests table1160 in FIG. 11B) the source resource “yahoo.com” on which the encodedlink linked to the “mtv.com” was presented, the cookie management engine1120 may identify the content publisher ID “3” corresponding to thesource resource “yahoo.com” by looking up the content publisher table1167.

In some embodiments, referring to FIGS. 11A and 11B, the cookiemanagement engine 1120 may further determine that a request includes acookie of the linking system 120, and responsive to determining that therequest does not include a cookie of the linking system, provide, to theclient device, a unique cookie of the linking system. For example, thecookie management engine 1120 may receive a request from a first clientdevice for access to the first resource 1145 a and identify, responsiveto receiving the request, from the request a cookie of the linkingsystem that is unique to the first client device. The cookie managementengine 1120 may generate a unique cookie of the linking systemresponsive to determining that the request does not include a cookie ofthe linking system, and provide to the first client device, thegenerated unique cookie.

In some embodiments, referring to FIG. 11A, the system or server 1100 ofthe linking system 120 may receive a plurality of requests to accessdestination resources of a plurality of content publishers viainteractions with encoded links linked to the destination resources, theencoded links generated by the linking system and presented on sourceresources. In some embodiments, the server may store, for each cookie ofthe plurality of cookies, in memory, data indicating i) the destinationresources of the plurality of content publishers accessed by the clientdevice of the cookie, ii) the source resources on which encoded linkslinked to the resources accessed by the client device of the cookie; andiii) second resources accessed by the client device of the cookie. Forexample, in some embodiments, referring to FIG. 11B, the server maystore in memory (e.g., as a data report 1156), for the cookie with ID“C1”, data indicating i) the destination resources of the plurality ofcontent publishers accessed by the client device of the cookie (e.g.,“mtv.com”), ii) the source resources on which encoded links linked tothe resources accessed by the client device of the cookie (e.g.,“yahoo.com” and “facebook.com”), and iii) second resources accessed bythe client device of the cookie (e.g., “abc.com”). Similarly, in someembodiments, referring to FIG. 11B, the server may store in memory(e.g., as the data report 1156), for the cookie with ID “C2”, dataindicating i) the destination resources of the plurality of contentpublishers accessed by the client device of the cookie (e.g.,“mtv.com”), ii) the source resources on which encoded links linked tothe resources accessed by the client device of the cookie (e.g.,“dogs.com” and “cats.com”), and iii) second resources accessed by theclient device of the cookie (e.g., Null for the cookie with ID “C2”).

Referring to FIG. 11C, at step 1184, the server may identify secondcontent publishers having resources that were accessed by client devicescorresponding to the cookies via encoded links generated by the server.In some embodiments, the content publisher identification engine 1125may identify second content publishers having resources that wereaccessed by the same client devices corresponding to the plurality ofcookies. In some embodiments, the identification may be performed byissuing a database query to the database 230, e.g., SQL SELECTstatement, on the resource requests table 1160. For example, referringto FIG. 11, by selecting all tuples having the cookie ID “1” from theresource requests table 1160, the identification engine 1125 mayidentify two content publishers, those with content publisher ID “1” and“2”, having the resources “mtv.com” and “abc.com” that were accessed bythe same first client device corresponding to the cookie ID “1”.

Referring to FIG. 11C, at step 1186, the server may provide, to thefirst content publisher, data corresponding to the identified secondcontent publishers having resources accessed by client devices that alsoaccessed the resources of the first content publisher. In someembodiments, the content publisher identification engine 1125 mayprovide, to a first content publisher, data corresponding to identifiedcontent publishers having resources accessed by the same client devicesthat also accessed resources of the first content publisher. Forexample, referring to FIG. 11B, by selecting all tuples having thecookie ID “1” and not having the destination content publisher ID “1”,the content publisher identification engine 1125 may provide, to thefirst content publisher with ID “1”, data corresponding to theidentified content publishers (e.g., the content publisher with ID “2”)having resources (e.g., “abc.com”) accessed by the same first clientdevice that also accessed resources (e.g., “mtv.com”) of the contentpublisher with ID “1”.

In some embodiments, referring to FIGS. 11A and 11B, the contentpublisher identification engine 1125 may provide, to a first contentpublisher, data corresponding to identified source content publishershaving resources identified by the source URLs that are accessed by theclient devices that also accessed (destination) resources of the firstcontent publisher. For example, referring to the resource request table1160 in FIG. 11B, the content publisher identification engine 1125 mayidentify all source URLs that are accessed by the first client devicecorresponding to cookie ID “1” that also accessed resources of the firstcontent publisher with ID “1” by selecting data in the source resourcefield 1162 of all tuples having “1” in the cookie ID field and “1” inthe destination content publisher. As a result, the content publisheridentification engine 1125 may identify the resources “yahoo.com” and“facebook.com” as source URLs that are accessed by the first clientdevice that also accessed resources of the first content publisher.

In some embodiments, referring to FIGS. 11A and 11B, the contentpublisher identification engine 1125 may receive, from the first contentpublisher 1150 a, a request to identify content publishers that haveresources that have been accessed by client devices that also accessedthe first content publisher 1150 a. In some embodiments, the firstcontent publisher 1150 a may use an application including a browser tosend a resource identification request to the content publisheridentification engine 1125. In some embodiments, the application may bea mobile application, generally referred to as an app, executing on amobile device, such as a smart phone or tablet device. In someembodiments, the application may include an add-on, extension, script,ActiveX control, applet, widget or other types and forms of executableinstructions executed by or in a browser. In some embodiments, theapplication may include, use or call one or more APIs to the contentaudience analysis system 1100. The application may be programmed toprogrammatically integrate the content audience analysis system 1100, orinterface thereto, into the application. Via the one or more APIs, theapplication may access data from the content audience analysis system1100. Via the one or more APIs, the application may perform or executeany of the functions or operations of the content audience analysissystem 1100. Via the one or more APIs, the application may perform orexecute any of the systems and methods described herein. In someembodiments, responsive to the resource identification request from aparticular content publisher, the content publisher identificationengine 1125 may identify content publishers that have resources thathave been accessed by client devices that also accessed the particularcontent publisher. In some embodiments, for example, referring to FIG.11B, the content publisher identification engine 1125 may identifycontent publishers that have resources that have been accessed by clientdevices that also accessed the (destination) content publisher with ID“P1”, by first selecting data in the cookie ID field 1161 of all tupleshaving “P1” in the destination content publisher field. Next, responsiveto identification of client devices with cookie IDs “C1” and “C2” as aresult of the selection, the content publisher identification engine1125 may further select data in the destination content publisher IDfield 1165 of all tuples having “C1” or “C2” in the cookie ID field.Next, as a selection result, the content publisher identification engine1125 may identify two destination content publishers with IDs “P1” and“P2”. Finally, the content publisher identification engine 1125 mayidentify the content publisher with ID “P2” as a content publisher(other than the content publisher with ID “1”) that has resources thathave been accessed by the client devices that also accessed the(destination) content publisher with ID “P1”. In some embodiments, thecontent publisher identification engine 1125 may issue a database querystatement to identify the result.

In some embodiments, the server may provide to a content publisher ofthe plurality of content publishers (e.g., the content publisher with ID“P1”), statistics derived from the stored data. For example, forproviding to the content publisher with ID “P1”, the statistics mayinclude i) the number of unique source resources on which encoded linkslinked to the destination resources of the content publisher with ID“P1” were presented (e.g., four unique source resources—“yahoo.com”,“facebook.com”, “dogs.com” and “cats.com”); ii) the most frequent sourceresource on which encoded links linked to the destination resources ofthe content publisher with ID “P 1” were presented; iii) the mostfrequent content publisher having resources on which encoded linkslinked to the destination resources of the content publisher with ID“P1” were presented (e.g., the most frequent content publisher havingsource resources is the content publisher with ID “P5” having two sourceresources “dogs.com” and “cats.com”; and iv) the number of other contentpublishers that are accessed by client devices that also accesseddestination resources of the content publisher with ID “P1” (e.g., thereis only one other content publisher (the one with ID “P2”) that isaccessed by the same client device(s) that also accessed destinationresources of the content publisher with ID “P1”).

L. Systems and Methods for Benchmarking Online Activity Via EncodedLinks

Systems and methods of benchmarking online activity via encoded linksgenerated by a linking system via encoded links are described. Thepresent solution includes an online activity benchmarking systemconfigured to identify multiple encoded links that are each generated bythe online activity benchmarking system and linked to an informationresource. The online activity benchmarking system can identify, from anactivity log maintaining entries corresponding to request to accessencoded links generated by the online activity benchmarking system, aplurality of requests to access a link of the identified multipleencoded links that are linked to the information resource. The onlineactivity benchmarking system can identify one or more attributescorresponding to each request of the plurality of requests to access theinformation resource via the encoded links to the information resource.The online activity benchmarking system may then categorize the requestsbased on the identified attributes and provide a statistics outputcorresponding to the categorized requests. Generally, the identifiedencoded links linked to the information resource may allow the onlineactivity benchmarking system to track statistics for each requestreceived via the identified encoded links, categorize the requests basedon one or more attributes, and provide output statistics correspondingto the categorized requests. For example, statistics of requestsreceived via encoded links can be calculated by analyzing userinteractions (e.g., clicks) on corresponding encoded links. Theinformation can be categorized or tabulated to generate statistics.

As described herein, the activity log can store data corresponding toeach request. The activity log can store, for each entry correspondingto a request to access an encoded link, data identifying one or moreattributes of the request. Examples of the attributes of the request caninclude i) a source resource on which the encoded link is presented whenaccessed, ii) a destination resource to which the encoded link islinked, iii) a time of the request; iv) a cookie of the online activitybenchmarking system assigned to the client device from which the requestwas received; v) a topic of the source resource; vi) a topic of thedestination resource, vii) a type of client device accessing thecontent; viii) the location or IP address of the client device, amongothers.

The online activity benchmarking system can perform analysis of the datato identify certain trends, for example, attributes or characteristicsof popular links and unpopular links. The online activity benchmarkingsystem can further generate recommendations based on such trends. In oneexample, a first information resource can have multiple encoded linkswhich can be posted on webpages in various verticals (e.g., sports, newsand music). Based on the statistics of requests received via each of theencoded links, the online activity benchmarking system 1200 canbenchmark the performance of the first information resource across thedifferent verticals, device types or geolocations of the users, amongothers.

Referring to FIG. 12A, the online activity benchmarking system 1200 canbe a part of the linking system 120 or may include components andfunctionality of the linking system 120, as shown in FIGS. 2A-11C. Theonline activity benchmarking system 1200 may include a link generator1245, a request manager 1250, a log manager 1255 and a content analyzer1260. The request manager 1250 can include a cookie management engine902 and an attributes identifier 1254. The online activity benchmarkingsystem 1200 also can include a database 230 described herein withrespect to FIGS. 2A-12B.

The link generator 1245 can include one or more executable instructions,code, scripts, programs, or modules configured to receive requests togenerate encoded links. In some implementations, the link generator 1245can receive a request from a client device to generate an encoded link.The request can include a resource identifier that is associated with aresource or content for which to generate the link. For instance, acontent publisher, via a client device of the content publisher, cantransmit a request to the link generator 1245 to generate a link to aparticular resource. The particular resource can be a resource hosted ona server of the content publisher (or accessible via a domain of thecontent publisher), or in some implementations, a resource hosted on aserver of any other content publisher or accessible via any otherdomain. In some embodiments, users may request to generate encoded linksto information resources, for instance, to share on a social mediawebsite. The link generator 1245 can generate the encoded link. Theencoded link corresponds to a resource maintained by the online activitybenchmarking system 1200 and configured such that when a request toaccess the encoded link is received by the online activity benchmarkingsystem 1200, the online activity benchmarking system 1200 can transmit aresponse to the client device requesting access redirecting the clientdevice to the resource to which the encoded link is linked. The onlineactivity benchmarking system 1200 is configured to store data associatedwith the request and can use the information associated with the requestto track online activity, as will be described herein. In someimplementations, the link generator 1245 can include one or morecomponents of other engines, modules, or components described withrespect to the linking system 120 described with respect to FIGS.2A-11C.

The link generator 1245 or the online activity benchmarking system 1200can maintain a plurality of encoded links and resources associated withthe encoded links that are configured to cause client devices requestingto access the encoded links to be redirected to other resources to whichthe encoded links are linked. In some implementations, the onlineactivity benchmarking system 1200 can maintain a database, such asdatabase, that can be configured to maintain a mapping of encoded linksto resources to which to redirect client devices accessing the encodedlinks.

The request manager 1250 can include one or more executableinstructions, code, scripts, programs, or modules configured to receivea request from a content publisher to provide statistical informationrelating to one or more information resources of the content publisher.The request manager 1250 can be configured to identify, for aninformation resource of the content publisher, a plurality of encodedlinks generated by the link generator 1245 that are linked to theinformation resource. In some embodiments, the request manager 1250 canperform a lookup in a data structure maintaining a mapping of encodedlinks to information resources to identify each of the encoded linkslinked to the information resource. For instance, if the informationresource has a resource identifier examplel.com, the request manager1250 can perform a lookup to identify each of the encoded links that arelinked to the resource identifier examplel.com.

The request manager 1250 can be configured to identify, for each of theidentified plurality of encoded links, a plurality of requests to accessthe information resource. The request manager 1250 can perform a lookupin a data structure that maintains an activity log of all of therequests to access encoded links generated by the link generator 1245.The activity log can be updated to maintain request related informationin the activity log for each request to access an information resourcevia an encoded link generated by the link generator 1245.

The log manager 1255 can include one or more executable instructions,code, scripts, programs, or modules configured to create, maintain,manager and update an activity log. The activity log can include one ormore data structures stored in the database 230 and can include datacorresponding to link activity 1210. In some embodiments, the logmanager 1255 of the online activity benchmarking system 1200 canidentify link activity 1210. Link activity can include any actionsperformed by users of one or more client devices on encoded linksgenerated by the link generator 1255. Examples of actions can includeclicking on encoded links, sharing encoded links, requesting to generatethe encoded links, among others. The log manager 1255 can identify theseactions and record the actions in the database 230. In some embodiments,the database 230 can include one or more data structures for recordinglink activity. For instance, each time a client device requests access(for instance, by clicking) to a link generated by the link generator1255, the log manager 1255 can identify the activity (for instance, therequest to access the link) and update one of the data structures toinclude the identified activity.

The activity log can be configured to include an entry for each requestto access an information resource via an encoded link generated by thelink generator 1245. The log manager 1255 can be configured to insert anentry in the activity log responsive to the online activity benchmarkingsystem 1200 receiving a request to access an information resource via anencoded link generated by the link generator 1245. Each entry caninclude various types of information. In some embodiments, each entrycan include values corresponding to a plurality of attributes acrosswhich different information resources of content publishers can bebenchmarked.

The activity log can store data corresponding to each request. Theactivity log can store, for each entry corresponding to a request toaccess an encoded link, data identifying one or more attributes of therequest. Examples of the attributes of the request can include i) asource resource on which the encoded link is presented when accessed,ii) a destination resource to which the encoded link is linked, iii) atime of the request; iv) a cookie of the online activity benchmarkingsystem assigned to the client device from which the request wasreceived; v) a topic of the source resource; vi) a topic of thedestination resource, vii) a type of client device accessing thecontent; viii) the location or IP address of the client device, amongothers.

The cookie management engine 902 may comprise an application, program,library, service, script, process, task or any type and form ofexecutable instructions executing or executable on a device. The cookiemanagement engine 902 may comprise logic, function or operations tocreate a cookie relating to a particular client device or application orprogram (e.g., browser) in a client device, and store, update andretrieve the cookie. More particularly, the cookie generator 904 can beconfigured to create a cookie relating to the particular client deviceor application or program in a client device, and to update the cookie.The cookie management engine 902 can create a plurality of cookiesrelating to respective client devices or applications or programs (e.g.,browsers) in a client device, and store, update and retrieve theplurality of cookies.

The cookie management engine 902 can be configured to correlate a clientdevice or application or program to a plurality of source resources anddestination resources to which the same client device or application orprogram has provided requests to access resources linked to encodedlinks generated by the online activity benchmarking system 1200. In someembodiments, to perform such correlations, the cookie management engine902 may comprise the link decoder 212 configured to decode an encodedURL, such as via the database 230 and perform a lookup of the URLcorresponding to the shortened or encoded URL (see FIG. 2). The cookiemanagement engine 902 may also correlate the client devices orapplications or programs to source resources from which the same clientdevice or application or program has generated request to access anotherresource (by clicking encoded links on those source resources by usersof the client devices or applications or programs).

In some embodiments, to perform such correlations, the cookie managementengine 902 can be configured to identify source resource informationfrom a header of a request generated by a client device (e.g., the HTTPheader field of referrer). For example, referring to FIG. 9A, responsiveto a request to access a second resource via an encoded link, the cookiemanagement engine 902 may generate a cookie correlating the clientdevice to both a first resource from which the client device generated arequest to access the second resource and the second resource.Responsive to the online activity benchmarking system receiving asubsequent request to access a third resource via another encoded linkfrom the same client device, the cookie management engine 902 may updatethe same cookie assigned to the client device so as to correlate theclient device to the first resource, second resource and the thirdresource.

The attributes identifier 1254 of the request manager 1250 can beconfigured to identify, for each request to access the informationresource via the plurality of encoded links, values of one or moreattributes corresponding to the request. The attributes identifier 1254can identify the values of one or more attributes from the entries ofthe activity log that correspond to the identified plurality ofrequests. In some embodiments, the attributes identifier 1254 canidentify values of a subset or portion of the attributes for whichbenchmarking can be performed. In some embodiments, depending on therequest to benchmark data, the attributes for which benchmarking is tobe performed per the request are included in the request to benchmark.

In some embodiments, the cookie management engine 902 of the requestmanager 1250 can identify, from the multiple requests to access theinformation resource for which a plurality of encoded links wereidentified by the request manager 1250, multiple cookies generated bythe cookie management engine 902 that are assigned to unique clientdevices. The attributes identifier 1254 can identify, from a cookiecorresponding to each request, one or more attributes corresponding tothe request. For example, the attributes identifier 1254 can identify,from a cookie relating to a particular client device, a plurality ofresources accessed by the particular client device and identify one ormore characteristics unique to the particular client device (asattributes of each request) by classifying the accessed plurality ofresources. In some embodiments, the identification of client devicecharacteristics can be performed by classification based on keywords ordomains or URLs extracted from the accessed plurality of resources or alist of phrases extracted from a click history stored in the cookie. Forexample, referring to FIG. 12A, the attributes identifier can identify,from a cookie 1225 corresponding to the request 1220, a plurality ofresources accessed by a first client device corresponding to the cookie1225. The attributes identifier can identify characteristics of thefirst client device (e.g., preference for a sports vertical) as anattribute for the request 1220 by classifying the identified pluralityof resources. Similarly, the attributes identifier can identify, from acookie 1235 corresponding to the request 1230, a plurality of resourcesaccessed by a second client device corresponding to the cookie 1235. Theattributes identifier can further identify characteristics of the secondclient device (e.g., preference for a music vertical) as an attributefor the request 1230 by classifying the identified plurality ofresources.

The content analyzer 1260 can include one or more executableinstructions, code, scripts, programs, or modules configured tocategorize the requests to access the information resource via theencoded links based on the one or more identified attributes. Thecontent analyzer 1260 can categorize the requests by sorting therequests according to one or more attributes. For instance, requeststhat were received when the client devices transmitting the request wereaccessing a first information resource can be categorized or groupedtogether, while requests that were received when the client devicestransmitting the request were accessing a second information resourcecan be categorized or grouped together. In some embodiments, requeststhat were received from mobile phones can be assigned to a particularcategory, while requests that were received from laptops (or non-mobilecomputing devices) can be assigned to another category, among others.The categories across which requests can be categorized is based on theattributes and the values of the attributes that are stored in theactivity log.

The content analyzer 1260 can further be configured to generate one ormore reports for presentation that presents graphics or any othercontent that provides statistics based on the requests to access theinformation resource via the encoded links linked to the informationresource. The content analyzer 1260 can generate the report responsiveto a request from a content publisher to provide statistics related tothe information resource. In some embodiments, the content analyzer 1260can generate a report comparing the performance of a first informationresource and a second information resource based on determiningstatistics from requests to access each of the first informationresource and the second information resource via respective encodedlinks generated by the link generator 1245.

In some embodiments, the content analyzer 1260 can determine a firstnumber of requests to access the information resource via encoded linksof the online activity benchmarking system 1200 that are displayed onwebpages corresponding to the first vertical. Similarly, the contentanalyzer 1260 can determine a second number of requests to access theinformation resource via encoded links of the online activitybenchmarking system 1200 that are displayed on webpages corresponding toa second vertical. The content analyzer can determine to which verticala webpage belongs based on an analysis of the domain name, otherwebpages of the domain, keywords in the webpage, identity of users thataccess the information resource, and via cookies assigned to the users,identity of other webpages the users have visited, among others. Thecontent analyzer 1260 can then provide, for presentation, a visualcontent item (e.g., graphs, charts or other graphical representations ofthe statistics output 1265) based on at least one of the first number ofrequests or the second number of requests. For example, the contentanalyzer can provide the first number of requests via encoded linkspresent on webpages corresponding to the first vertical (e.g., sports)and the second number of requests via encoded links present on webpagescorresponding to the second vertical (e.g., music) per different typesof client devices performing each request (e.g., mobile devices anddesktop devices) during different months (e.g., September 2014, October2014, November 2014, and December 2014).

Referring now to FIG. 12B, an example statistics output of an embodimentof the content analyzer 1260 for providing a visual content item isdepicted. In some embodiments, as shown in FIG. 12B, the contentanalyzer 1260 can provide, for presentation, a bar chart 1296 of thestatistics output 1265 including a first number of requestscorresponding to the first vertical (e.g., sports) and a second numberof requests corresponding to the second vertical (e.g., music). In someembodiments, the content analyzer 1260 can provide a user interface 1280to select different granularity of time periods, for example, all time1282, yearly 1283, monthly 1284 and hourly 1285. In some embodiments,the user interface 1280 can include an interface 1281 to select a customperiod (e.g., from January 2014 to December 2014). In some embodiments,the user interface 1280 can include web-based user interfaces, e.g.,tabs (as shown in FIG. 12B), radio buttons, dropdown lists, list boxes,buttons, dropdown buttons, etc. In some embodiments, as shown in FIG.12B, when “monthly” time period is selected, the content analyzer 1260can provide a bar chart of the statistics output 1265 including thenumber of requests per different verticals 1294 (e.g., sports and music)during different monthly time periods of requests 1292 (e.g., September2014, October 2014, November 2014, and December 2014).

In some embodiments, the content analyzer 1260 can rank each of thefirst and second encoded links based on their respective number ofrequests across the at least one attribute (e.g., per differentverticals, per different time periods during which each request isperformed, per different types of a client device performing eachrequest, etc.).

In some embodiments, the content analyzer 1260 can determine a firstnumber of requests to access the information resource via encoded linksof the online activity benchmarking system 1200 that are received from afirst referral source. In some embodiments, the first referral sourcecan be determined based on a source URL on which each request isperformed. Similarly, the content analyzer 1260 can determine a secondnumber of requests to access the information resource via encoded linksof the online activity benchmarking system 1200 that are received from asecond referral source. The content analyzer 1260 can provide, forpresentation a visual content item based on at least one of the firstnumber of requests or the second number of requests. In someembodiments, the content analyzer 1260 can then provide, forpresentation, a visual content item (e.g., graphs, charts or othergraphical representations) based on at least one of the first number ofrequests performed on the first referral source or the second number ofrequests performed on the second referral source.

Referring now to FIG. 12C, an embodiment of a method for benchmarkingonline activity via encoded links is depicted. In brief overview, atstep 1270, the system or server 1200 may identify, for an informationresource, a plurality of encoded links encoded by the server of theonline activity benchmarking system 1200 and linked to the informationresource. At step 1272, the server 1200 may receive via the identifiedplurality of encoded links, a plurality of requests to access theinformation resource. At step 1274, the server 1200 may identify foreach request of the plurality of requests to access the informationresource, one or more attributes corresponding to the request. At step1276, the server 1200 may categorize the plurality of requests to accessthe information resource based on the one or more identified attributes.At step 1278, the server 1200 may provide for presentation, an outputindicating statistics corresponding to the categorized plurality ofrequests based on the one or more identified attributes.

More particularly, at step 1270, the system or server 1200 may identify,for an information resource, a plurality of encoded links encoded by theserver of the online activity benchmarking system 1200 and linked to theinformation resource. The link generator 1245 or the online activitybenchmarking system 1200 can maintain a plurality of encoded links andresources associated with the encoded links that are configured to causeclient devices requesting to access the encoded links to be redirectedto other resources to which the encoded links are linked. In someimplementations, the online activity benchmarking system 1200 canmaintain a database, such as database, that can be configured tomaintain a mapping of encoded links to resources to which to redirectclient devices accessing the encoded links.

The request manager 1250 can receive a request from a content publisherto provide statistical information relating to one or more informationresources of the content publisher. The request manager 1250 can beconfigured to identify, for an information resource of the contentpublisher, a plurality of encoded links generated by the link generator1245 that are linked to the information resource. In some embodiments,the request manager 1250 can perform a lookup in a data structuremaintaining a mapping of encoded links to information resources toidentify each of the encoded links linked to the information resource.For instance, if the information resource has a resource identifierexamplel.com, the request manager 1250 can perform a lookup to identifyeach of the encoded links that are linked to the resource identifierexamplel.com.

At step 1272, the server 1200 may receive via the identified pluralityof encoded links, a plurality of requests to access the informationresource. Referring to FIG. 12A, for example, the request manager 1250can receive, via the identified multiple encoded links, a request 1220and a request 1230, both to access the same first resource 1240. Therequest manager 1250 can be configured to identify, for each of theidentified plurality of encoded links, a plurality of requests to accessthe information resource. The request manager 1250 can perform a lookupin a data structure that maintains an activity log of all of therequests to access encoded links generated by the link generator 1245.The activity log can be updated to maintain request related informationin the activity log for each request to access an information resourcevia an encoded link generated by the link generator 1245. In someembodiments, the request manager 1250 of the online activitybenchmarking system 1200 can identify link activity 1210. Link activitycan include any actions performed by users of one or more client deviceson encoded links generated by the link generator 1255. Examples ofactions can include clicking on encoded links, sharing encoded links,requesting to generate the encoded links, among others. The onlineactivity benchmarking system 1200 can identify these actions and recordthe actions in the database 230. In some embodiments, the database 230can include one or more data structures for recording link activity. Forinstance, each time a client device requests access (for instance, byclicking) to a link generated by the link generator 1255, the requestmanager 1250 can identify the activity (for instance, the request toaccess the link) and update one of the data structures to include theidentified activity.

The activity log can be maintained in the database 230 by the onlineactivity benchmarking system 1200. The activity log can be configured toinclude an entry for each request to access an information resource viaan encoded link generated by the link generator 1245. A log manager (notshown) can be configured to insert an entry in the activity logresponsive to the online activity benchmarking system 1200 receiving arequest to access an information resource via an encoded link generatedby the link generator 1245. Each entry can include various types ofinformation. In some embodiments, each entry can include valuescorresponding to a plurality of attributes across which differentinformation resources of content publishers can be benchmarked.

The activity log can store data corresponding to each request. Theactivity log can store, for each entry corresponding to a request toaccess an encoded link, data identifying one or more attributes of therequest. Examples of the attributes of the request can include i) asource resource on which the encoded link is presented when accessed,ii) a destination resource to which the encoded link is linked, iii) atime of the request; iv) a cookie of the online activity benchmarkingsystem assigned to the client device from which the request wasreceived; v) a topic of the source resource; vi) a topic of thedestination resource, vii) a type of client device accessing thecontent; viii) the location or IP address of the client device, amongothers.

At step 1274, the server 1200 may identify for each request of theplurality of requests to access the information resource, one or moreattributes corresponding to the request. The request manager 1250 can beconfigured to identify, for each request to access the informationresource via the plurality of encoded links, values of one or moreattributes corresponding to the request. The request manager 1250 canidentify the values of one or more attributes from the entries of theactivity log that correspond to the identified plurality of requests. Insome embodiments, the request manager 1250 can identify values of asubset or portion of the attributes for which benchmarking needs to beperformed. In some embodiments, depending on the request to benchmarkdata, the attributes for which benchmarking needs to be performed areincluded in the request to benchmark. In some embodiments, theidentified one or more attributes can include a type of the clientdevice, a geographic location of the client device, a time of day atwhich the request is performed, or a source Uniform Resource Locator(URL) on which the request is performed. In some embodiment, thegeographic location of the client device can be identified from a domainof the client device.

At step 1276, the server 1200 may categorize the plurality of requeststo access the information resource based on the one or more identifiedattributes. The content analyzer 1260 can categorize the requests toaccess the information resource via the encoded links based on the oneor more identified attributes. The content analyzer 1260 can categorizethe requests by sorting the requests according to one or moreattributes. For instance, requests that were received when the clientdevice transmitting the request was accessing a first informationresource can be categorized or grouped together, while requests thatwere received when the client device transmitting the request wasaccessing a second information resource can be categorized or groupedtogether. In some embodiments, requests that were received from a mobilephone can be assigned to a particular category, while requests that werereceived from a laptop can be assigned to another category, amongothers. The categories across which requests can be categorized is basedon the attributes and the values of the attributes that are stored inthe activity log.

At step 1278, the server 1200 may provide for presentation, an outputindicating statistics corresponding to the categorized plurality ofrequests based on the one or more identified attributes. The contentanalyzer 1260 can generate one or more reports for presentation thatpresents graphics or any other content that provides statistics based onthe requests to access the information resource via the encoded linkslinked to the information resource. The content analyzer 1260 cangenerate the report responsive to a request from a content publisher toprovide statistics related to the information resource. In someembodiments, the content analyzer 1260 can generate a report comparingthe performance of a first information resource and a second informationresource based on determining statistics from requests to access each ofthe first information resource and the second information resource viarespective encoded links generated by the link generator 1245.

For example, the content analyzer 1260 can provide a statistics outputcorresponding to multiple requests based on different verticals anddifferent request time periods as attributes. In some embodiments, thecontent analyzer 1260 can determine a first number of requests to accessthe information resource via encoded links of the online activitybenchmarking system that are displayed on webpages corresponding to thefirst vertical. Similarly, the content analyzer 1260 can determine asecond number of requests to access the information resource via encodedlinks of the online activity benchmarking system that are displayed onwebpages corresponding to a second vertical. The content analyzer 1260can then provide, for presentation, a visual content item (e.g., graphs,charts or other graphical representations of the statistics output 1265)based on at least one of the first number of requests or the secondnumber of requests. For example, the content analyzer can provide thefirst number of requests via encoded links present on webpagescorresponding to the first vertical (e.g., sports) and the second numberof requests via encoded links present on webpages corresponding to thesecond vertical (e.g., music) per different types of client devicesperforming each request (e.g., mobile devices and desktop devices)during different months (e.g., September 2014, October 2014, November2014, and December 2014).

It should be understood that any of the systems described above mayprovide multiple ones of any or each of those components and thesecomponents may be provided on either a standalone machine or, in someembodiments, on multiple machines in a distributed system. The systemsand methods described above may be implemented as a method, apparatus orarticle of manufacture using programming and/or engineering techniquesto produce software, firmware, hardware, or any combination thereof. Inaddition, the systems and methods described above may be provided as oneor more computer-readable programs embodied on or in one or morearticles of manufacture. The term “article of manufacture” as usedherein is intended to encompass code or logic accessible from andembedded in one or more computer-readable devices, firmware,programmable logic, memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs,SRAMs, etc.), hardware (e.g., integrated circuit chip, FieldProgrammable Gate Array (FPGA), Application Specific Integrated Circuit(ASIC), etc.), electronic devices, a computer readable non-volatilestorage unit (e.g., CD-ROM, floppy disk, hard disk drive, etc.). Thearticle of manufacture may be accessible from a file server providingaccess to the computer-readable programs via a network transmissionline, wireless transmission media, signals propagating through space,radio waves, infrared signals, etc. The article of manufacture may be aflash memory card or a magnetic tape. The article of manufactureincludes hardware logic as well as software or programmable codeembedded in a computer readable medium that is executed by a processor.In general, the computer-readable programs may be implemented in anyprogramming language, such as LISP, PERL, C, C++, C#, PROLOG, or in anybyte code language such as JAVA or in any script language, such asPython or TCL. The software programs may be stored on or in one or morearticles of manufacture as object code.

While various embodiments of the methods and systems have beendescribed, these embodiments are exemplary and in no way limit the scopeof the described methods or systems. Those having skill in the relevantart can effect changes to form and details of the described methods andsystems without departing from the broadest scope of the describedmethods and systems. Thus, the scope of the methods and systemsdescribed herein should not be limited by any of the exemplaryembodiments and should be defined in accordance with the accompanyingclaims and their equivalents.

What is claimed is:
 1. A system, comprising: at least one server,configured to: receive, from a client device, a request to access afirst encoded link of the at least one server linked to a first resourceof a first content publisher, the request identifying i) a sourceuniform resource locator (URL) identifying a resource on which the firstencoded link was presented and ii) a cookie of the at least one serverthat is unique to the client device; identify, from the cookie of the atleast one server in the request to access the first resource via thefirst encoded link, a second content publisher having a second resourceaccessed by the client device via a second encoded link of the at leastone server; and store, on a database, an association between theidentified second content publisher having the second resource and thefirst content publisher having the first resource also accessed by theclient device.
 2. The system of claim 1, wherein the at least one serveris further configured to generate data corresponding to the associationbetween the second content publisher having the second resource accessedby the client device and the first content publisher.
 3. The system ofclaim 1, wherein the at least one server is further configured toprovide, for presentation, data corresponding to the association betweenthe second content publisher having the second resource accessed by theclient device and the first content publisher.
 4. The system of claim 1,wherein the at least one server is further configured to: determine acategory of content included in the resource on which the first encodedlink was presented; and store, on the database, the associationincluding the category of the content included in the resource on whichthe first encoded link was presented.
 5. The system of claim 1, whereinthe at least one server is further configured to: determine a categoryof content included in the first resource of the first content publisheraccessible via the first encoded link; and store, on the database, theassociation including the category of the content included in the firstresource of the first content publisher.
 6. The system of claim 1,wherein the at least one server is further configured to: determine acategory of content included in the second resource of the secondcontent publisher accessible via the second encoded link, the secondcontent publisher identified from the cookie; and store, on thedatabase, the association including the category of the content includedin the second resource of the second content publisher.
 7. The system ofclaim 1, wherein the at least one server is further configured to:generate a list of keywords from content of the first resource of thefirst content publisher accessible via the first encoded link; andstore, on the database, the association including the list of keywordsfrom the content of the first resource of the first content publisher.8. The system of claim 1, wherein the at least one server is furtherconfigured to generate a list of keywords from content of the secondresource of the second content publisher accessible via the secondencoded link, the second content publisher identified from the cookie;and store, on the database, the association including the list ofkeywords from the content of the second resource of the second contentpublisher.
 9. The system of claim 1, wherein the at least one server isfurther configured to: receive a query identifying at least one of thefirst content publisher, the second content publisher, or an identifiercorresponding to the cookie; and identify, from the database, using thequery, the association between the second content publisher having thesecond resource and the first content publisher having the firstresource.
 10. The system of claim 1, wherein the at least one server isfurther configured to identify the first encoded link generated by theat least one server and to identify the second encoded link generated bythe at least one server.
 11. A method, comprising: receiving, by atleast one server, from a client device, a request to access a firstencoded link of the at least one server linked to a first resource of afirst content publisher, the request identifying i) a source uniformresource locator (URL) identifying a resource on which the first encodedlink was presented and ii) a cookie of the at least one server that isunique to the client device; identifying, by the at least one server,from the cookie of the at least one server in the request to access thefirst resource vie the first encoded link, a second content publisherhaving a second resource accessed by the client device via a secondencoded link of the at least one server; and storing, by the at leastone server, on a database, an association between the identified secondcontent publisher having the second resource and the first contentpublisher having the first resource also accessed by the client device.12. The method of claim 11, further comprising generating, by the atleast one server, data corresponding to the association between thesecond content publisher having the second resource accessed by theclient device and the first content publisher.
 13. The method of claim11, further comprising providing, by the at least one server, forpresentation, data corresponding to the association between the secondcontent publisher having the second resource accessed by the clientdevice and the first content publisher.
 14. The method of claim 11,further comprising determining, by the at least one server, a categoryof content included in the resource on which the first encoded link waspresented; and wherein storing further comprises storing, on thedatabase, the association including the category of the content includedin the resource on which the first encoded link was presented.
 15. Themethod of claim 11, further comprising determining, by the at least oneserver, a category of content included in the first resource of thefirst content publisher accessible via the first encoded link; andwherein storing further comprises storing, on the database, theassociation including the category of the content included in the firstresource of the first content publisher.
 16. The method of claim 11,further comprising determining, by the at least one server, a categoryof content included in the second resource of the second contentpublisher accessible via the second encoded link, the second contentpublisher identified from the cookie; and wherein storing furthercomprises storing, on the database, the association including thecategory of the content included in the second resource of the secondcontent publisher.
 17. The method of claim 11, further comprisinggenerating, by the at least one server, a list of keywords from contentof the first resource of the first content publisher accessible via thefirst encoded link; and wherein storing further comprises storing, onthe database, the association including the list of keywords from thecontent of the first resource of the first content publisher.
 18. Themethod of claim 11, further comprising generating, by the at least oneserver, a list of keywords from content of the second resource of thesecond content publisher accessible via the second encoded link, thesecond content publisher identified from the cookie; and wherein storingfurther comprises storing, on the database, the association includingthe list of keywords from the content of the second resource of thesecond content publisher.
 19. The method of claim 11, furthercomprising: receiving, by the at least one server, a query identifyingat least one of the first content publisher, the second contentpublisher, or an identifier corresponding to the cookie; andidentifying, by the at least one server, from the database, using thequery, the association between the second content publisher having thesecond resource and the first content publisher having the firstresource.
 20. The method of claim 11, further comprising identifying, bythe at least one server, the first encoded link generated by the atleast one server and the second encoded link generated by the at leastone server.